Search Results (393)

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation. Full article

The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel surface defect detection model named DIDW-YOLOv11. In the proposed DIDW-YOLOv11, the YOLOv11 C3k2 module is first innovatively improved by C3K2-DIMB, which integrates C3K2 and DIMB by introducing DynamicInceptionDWConv2d (DIDW) to sufficiently strengthen the detailed feature extraction for tiny defects and weak-texture defects, improving the matching degree of multi-scale receptive fields. Then the YOLOv11 SPPF module is enhanced by integrating the IDWFSPPF module for optimizing the fusion of local and global information, which combines average pooling and max pooling to enhance the model’s multi-scale feature fusion capability. An auxiliary detection head (ADH) is finally proposed with an additional coarse loss function to process shallow feature information into the model, which uses extra supervision for shallow features to suppress background noise and reduce false detections. Experimental results on the NEU-DET and GC10-DET datasets show that DIDW-YOLOv11 achieves 4.9% and 3.8% improvements in mAP@0.5 compared to the baseline model YOLOv11s. Our research indicates that DIDW-YOLOv11 exhibits stronger recognition ability and robustness in complex and diverse defect detection, providing an effective solution for steel defect detection in industrial production. In addition, experimental results show that our model offers improved performance over the baseline methods. Full article

Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R² was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation. Full article

The increasing use of UAVs has raised concerns regarding public safety and airspace security. To address air-to-air micro-UAV detection with cluttered backgrounds, tiny targets, and diverse viewing angles, this paper develops SPAE-YOLOv8, a lightweight detector based on YOLOv8n. SPAE consists of four core designs: SIoU loss, P2 shallow feature layer, ADown adaptive downsampling, and Efficient_UAVDet lightweight detection head. These modules improve small-target representation and reduce model size. In this paper, lightweight refers to the combination of parameter count, storage volume and inference speed. On the Det-Fly dataset, the proposed method achieves an mAP@0.5 of 0.922, outperforming YOLOv8n by 7.2 percentage points while reducing total parameters by 30%. We conduct independent training and testing on the DUT Anti-UAV dataset and obtain an mAP@0.5 of 0.906. Cross-dataset testing is further carried out on the more challenging Anti-UAV300 dataset without additional fine-tuning to verify the generalization performance of the model. In real-world onboard deployment, the model is implemented on an Intel NUC11TNHi7 embedded UAV platform with OpenVINO acceleration and achieves 43.9 FPS at a resolution of $640\times 640$, satisfying real-time inference requirements. The ablation results demonstrate the contribution of the proposed modules, providing an efficient lightweight solution for airborne monitoring and civil airspace security. Full article

With the rapid expansion of global photovoltaic (PV) installed capacity, hot spot defects have become a major hidden danger that reduces power generation efficiency and threatens the safe and stable operation of PV stations. Unmanned aerial vehicle (UAV) infrared remote sensing is a key technology for the efficient intelligent monitoring of large-scale PV stations. However, detecting tiny hotspots in such infrared images poses severe challenges. Most of these defects are ultra-small targets with extremely low pixel size and weak contrast, which are easily submerged by complex background noise, leading to prominent issues including low detection accuracy and high miss rates. To address these issues, we propose a lightweight detection network based on YOLO11n, named PHSNet, for PV hotspot detection in UAV infrared images. Its core designs include the dynamic convolution integrated C3k2 (Dy-C3k2) for small target feature enhancement, context-guided downsampling (CG-Down) to alleviate feature loss during downsampling, optimized detection layers, and a lightweight shared deconvolutional detection head (LSDECD) for small target adaptation in low-altitude aerial scenes, forming a full-link optimization architecture for tiny target feature perception. Experiments on a dedicated dataset (4025 images, 25,181 annotations, 92% targets < 20 pixels) show that PHSNet achieves 0.73 AP₅₀ and 0.315 AP, surpassing YOLO11n by 0.1 in AP₅₀ and 0.058 in AP, respectively. With only 1.8 M parameters and 98.8 FPS, it outperforms mainstream lightweight models, including YOLOv8n and RT-DETR-R18, strikes a superior accuracy–efficiency balance, and provides an efficient solution for real-time intelligent monitoring and edge deployment of PV stations. Full article

Aiming at the challenges of class imbalance, tiny defect scales, and complex brushed background interference in the surface defect detection of electrical commutators, this paper proposes a high-precision and lightweight improved instance segmentation algorithm named WG-YOLOv11. Firstly, to overcome the barrier of highly imbalanced positive and negative samples in actual industrial data collection, a Balanced Defect Synthesis (BDS) data augmentation strategy is introduced to effectively enrich the morphological diversity of tiny defects. Secondly, a Wavelet Transform Convolution (WTConv) module is collaboratively integrated into the feature extraction network to expand the receptive field while preserving the high-frequency edge details of hairline cracks. Thirdly, a Group CBAM Enhancer (GCE) module is introduced to filter out high-reflection and brushed background noise through grouped attention and weight re-calibration mechanisms. Finally, addressing the difficulty of pixel-level alignment for tiny defects, an $\alpha$-IoU loss function is utilized to improve the high-precision segmentation and localization capabilities by dynamically adjusting the gradient distribution. Comprehensive evaluations are conducted on two real-world electrical commutator surface defect datasets: KolektorSDD2 and KolektorSDD. Experimental results show that on the KolektorSDD2 dataset, compared to the YOLOv11 baseline, the Mask mAP@50 of WG-YOLOv11 increases from 85.2% to 89.2%, and the stringent metric Mask mAP@50:95 improves from 52.7% to 56.9%. Additional computational analysis on the same dataset validates that the proposed method maintains high efficiency, matching the baseline computational cost without compromising real-time inference speed. Furthermore, evaluations on the public MSD dataset confirm the model’s cross-domain generalization capabilities. The proposed framework effectively achieves a balance between detection accuracy, anti-interference robustness, and a lightweight architecture. Full article

Moisture damage is a common hidden distress in asphalt pavements in hot and rainy regions, where it can rapidly develop into severe surface deterioration if not detected in time. To address this issue, this study proposes an automated framework integrating ground-penetrating radar (GPR) data and the YOLOv13 model for multi-scale moisture damage detection on the Jingang Expressway in Cambodia. A total of 1672 GPR images containing moisture damage were collected through field surveys using a 2.3 GHz GPR system. Based on field statistical analysis, the detected damage was classified into three scale levels: large-scale (>2 m), medium-scale (0.8–2 m), and tiny-scale (<0.8 m). Several recent YOLO variants were compared, and YOLOv13s was identified as the optimal model, achieving the best balance between detection accuracy and inference efficiency, with an mAP@0.5 of 85.3% and an FPS of 48. The proposed method was further validated through laboratory and field tests. The results indicate that the developed framework can effectively detect and localize multi-scale moisture damage under practical engineering conditions, providing a non-destructive and efficient approach for pavement condition assessment in hot and rainy regions. By enabling early-stage detection of moisture damage deterioration, the proposed framework may contribute to more sustainable pavement maintenance and long-term transportation infrastructure management. Full article

Safety gear detection in complex industrial environments faces challenges such as strong background interference, multi-scale spatial perturbations, and the loss of small target features. Furthermore, existing attention-based object detection methods often struggle to balance fine-grained feature retention with background noise suppression. To address these issues, this paper proposes AGCA-YOLOv9, a lightweight object detection model (9.77 M parameters and 39.6 GFLOPs). The core contribution is the Adaptive Gated Coordinate Attention (AGCA) module integrated into the GELAN backbone. Unlike standard coordinate attention mechanisms, AGCA employs a dual-path hybrid pooling strategy combined with an adaptive gated weight fusion mechanism. This design dynamically regulates the synergy between global semantic information and local salient textures, differentiating it from traditional linear feature aggregation. Consequently, it effectively suppresses false detections caused by visually isomorphic backgrounds, such as dense steel frames, while enhancing the representation of distant tiny targets. Validation on the Safety Helmet and Reflective Jacket dataset and the Helmet-Vest-Belt dataset shows that, compared to the YOLOv9s baseline, AGCA-YOLOv9 increases the mAP@50:95 on the Safety Helmet and Reflective Jacket dataset by 0.6% (reaching 80.9%) and the recall rate by 0.4% (reaching 91.9%). Specifically, the mAP@50:95 for the safety helmet category improves by 0.8%. On the Helmet-Vest-Belt dataset, the mAP@50:95 increases by 1.5% (reaching 60.5%). The single-image inference time is 4.6 ms. These results indicate that the proposed algorithm achieves a practical trade-off between detection accuracy and real-time processing speed, demonstrating its potential for safety compliance monitoring in industrial scenarios. Full article

Steel surface quality critically determines the service safety and structural reliability of industrial products. Defects such as cracks, inclusions, patches, pitting, rolled-in scale, and scratches severely compromise product safety, making accurate and efficient detection a key step in quality control. However, the native A2C2f module in YOLOv13 exhibits insufficient multi-scale feature extraction for tiny defects and weak robustness under complex industrial backgrounds, hindering the detection of these six defect types. To address these gaps, we propose a multi-scale denoising enhanced module, A2C2f-MSDE, which constructs a multi-scale multi-kernel fusion branch (MSKF) with learnable adaptive weights, integrates a lightweight SEL channel attention and a DE denoising module, and employs a dual learnable residual scaling structure, while preserving the original multi-scale fusion architecture. We embed A2C2f-MSDE into the YOLOv13 backbone, perform ablation studies to verify each component’s contribution, compare it with mainstream advanced detectors on the public NEU-DET dataset, and conduct generalization tests on the GC10-DET dataset. Experiments on NEU-DET show that the improved YOLOv13n achieves mAP50-95 of 0.454 (9.4% relative gain over baseline, absolute gain 0.039), with mAP50 and mAP75 reaching 0.774 and 0.466, at an inference speed of 555 FPS, respectively, outperforming the compared mainstream models. On GC10-DET, mAP50 reaches 0.704, comparable to the baseline, maintaining stable overall detection capability, while mAP75 and mAP50-95 improve by 0.033 and 0.019, verifying the module’s performance advantages under high localization accuracy requirements and its cross-dataset generalization ability. The proposed module effectively balances detection accuracy and lightweight characteristics, providing a high-performance solution for industrial steel defect detection. Full article

To meet the demand for real-time and accurate diamond particle size measurement in industrial scenarios—where traditional image processing methods lack robustness in complex environments and existing deep learning models struggle to balance accuracy and efficiency—this paper proposes an integrated framework for dynamic segmentation and morphological analysis of diamond particles based on video streams. A fully automated data acquisition system consisting of a high-precision motion stage, an industrial camera, and an optical microscope is first constructed to capture dynamic particle images. Based on YOLOv8n-seg, a lightweight SPR-YOLOv8 instance segmentation model is then developed with three key improvements: a Large Separable Kernel Attention (LSKA) mechanism is introduced into the SPPF module to enhance feature discriminability; the RepBlock module is adopted in the neck network to improve feature fusion efficiency through structural re-parameterization; and a P2 small-object detection head is introduced while large-object detection branches are removed, enabling the model to focus on tiny, densely distributed particles. Finally, a contour-based geometric analysis method is proposed for particle size estimation based on segmentation results. Experimental results show that the proposed model achieves an mAP@0.9 of 0.861 while maintaining a low parameter count (0.97 M) and a high inference speed of 500 FPS. Compared with the conventional OpenCV-based method (CADPS), the proposed DPSCA framework reduces the mean absolute percentage error in particle size measurement by over 70%, while also demonstrating strong accuracy and stability in consecutive-frame tracking. Overall, this study provides a practical and efficient automated inspection solution for online quality control in superhard material manufacturing, and supplementary cross-scale experiments further demonstrate promising robustness on diamond particles beyond the primary 180–250 μm range. Full article

Steel surface defects in manufacturing are typically tiny, low-contrast, and boundary-ambiguous, especially under complex textures (e.g., rolling marks, crazing), poor illumination, and high noise. These characteristics cause frequent missed detections and localization errors, particularly for defects with large-scale variations. Existing detectors, including YOLOv11, lack sufficient local spatial modeling for deformed or blurred boundaries and suffer from limited cross-scale feature interaction, leading to suboptimal performance on industrial benchmarks. To overcome these limitations, we propose YOLOv11-LLR—a YOLOv11-based framework that jointly enhances multi-scale feature modeling and inference efficiency. YOLOv11-LLR synergistically integrates three modules: Deformable Large Kernel Attention (DLKA) for adaptive local spatial perception, Lightweight Group-wise Attention (LWGA) for cross-scale interaction, and Re-parameterized Convolution (RepConv) for deployment-friendly speed. We evaluate on two representative datasets: NEU-DET (six defect types on hot-rolled steel strips) and GC10-DET (ten defect types with higher background complexity). Compared to baseline YOLOv11, YOLOv11-LLR achieves +3.5% mAP@0.5 (80.2%→83.7%) and +2.4% mAP@0.5:0.95 (48.7%→51.1%) on NEU-DET, and larger gains of +9.8% (61.0%→70.8%) and +3.4% (33.4%→36.8%) on the more challenging GC10-DET. These results demonstrate that YOLOv11-LLR provides an effective, robust, and industrially deployable solution for steel surface defect detection under complex textures, noise, and multi-scale variations. Full article

Unmanned aerial vehicle (UAV) imagery offers a promising alternative to manual and vehicle-based inspection for highway pavement distress detection, but the high-angle perspective reduces the relative size and feature richness of small distresses and amplifies aliasing during downsampling, limiting the accuracy of existing detectors. To address these problems, this paper proposes an improved YOLOv8 algorithm with four coordinated modifications: (i) a Feature-Focusing Diffusion Pyramid Network (FFDPN) that replaces the conventional PAN to strengthen multi-scale feature fusion and preserve fine-grained details; (ii) an Information Interaction Detection Head (IIDH) that replaces the decoupled dual-branch head, sharing interaction features between the classification and regression branches via deformable convolution (DCNv2) to reduce parameters while improving task synergy; (iii) an Edge Information Extraction Module (EIEM) placed at the front of the backbone, which uses Sobel-based gradient response plus max-pooling to inject low-level edge priors; and (iv) a WaveletPool downsampling operator that decomposes features into LL/LH/HL/HH sub-bands to suppress aliasing of small-scale distresses. Experiments on 3408 UAV images of four distress categories (transverse, longitudinal, and alligator cracks and potholes) show that the improved model reaches 93.7% Precision, 89.6% Recall, and 96.0% mAP@0.50—a 12.2 percentage-point gain over YOLOv8n—while using only 2.41 × 10⁶ parameters and outperforming Faster R-CNN, DETR, YOLOv7-tiny, YOLOv9, YOLOv10n, YOLOv11n, and YOLO-World on the same benchmark. The model eliminates the duplicate and missed detections observed in baselines, at a moderate cost in FPS (30.3 vs. 57.1 for YOLOv8n). Full article

In drone aerial photography applications, small object detection is crucial. For instance, it enables locating missing individuals on the ground during search-and-rescue operations, identifying distant vehicles in traffic monitoring, and detecting early-stage pest infestations in agricultural fields. However, aerial images present a unique challenge: due to the high flight altitude of drones, targets occupy only a minimal pixel area. Combined with complex backgrounds and sparse features, small objects are easily obscured by surrounding environments. To address these issues, this paper proposes the EFPN-YOLO model based on YOLOv12n. First, we introduce the Feature-Sharing Convolution (FSConv) module, which extracts multi-scale features with low parameter requirements through shared convolution kernels and multi-scale sparse sampling. Second, by integrating deformable convolutions with a dual-channel attention mechanism, we develop the Enhanced Dual-Dimensional Calibration (EDDC) module, significantly improving spatial feature modeling capabilities and feature enhancement effectiveness. Finally, we construct the RC-FPN architecture, employing a bidirectional fusion structure and diagonal cross-layer skip connections to minimize information loss. Meanwhile, the Bottleneck structure in the C3K2 module is replaced with the RepViTBlock to construct the C3k2_RVB module, which enhances the multi-scale feature expression ability through a two-stage design of spatial and channel mixing. On the VisDrone2019 dataset, the model’s mAP50 improved from 33.9% to 40.7%; on the TinyPerson dataset, it rose from 13.9% to 19.2%; and on the NVIDIA Jetson Orin Nano 8 GB superplatform, the model achieved a frame rate (FPS) of 15. Experiments demonstrate that EFPN-YOLO excels in small object detection and holds significant practical value. Full article

Apple surface diseases are crucial factors affecting the quality and yield of apples. Traditional manual inspection methods suffer from low efficiency and poor real-time performance. To address these issues, this paper proposes an apple surface disease detection method based on an improved YOLOv11s. Firstly, three groups of GAM attention mechanisms are integrated into the neck structure of the YOLOv11s to enhance the efficiency of feature fusion and the capability of semantic information transmission. Secondly, the original convolutional downsampling in the backbone network is replaced with a Haar-based feature downsampling module, enabling the model to retain more high-frequency detail information during the downsampling process. In addition, the WFU module is introduced to realize the dynamic allocation of feature weights, enhancing the model’s ability to recognize multi-scale defect features. Finally, the PIOUv2 loss function is adopted to optimize bounding box regression, improving the model’s detection performance for tiny defect spots. In addition, various data augmentation methods for small datasets are employed to improve the model training performance and effectively avoid the problem of data overfitting. The experimental results demonstrate that the F1-score of the proposed model is increased by 4.2%, and the mAP@50:95 is boosted by 2.4%. The detection performance outperforms various comparative models, which verifies the effectiveness and superiority of the proposed method. Full article

As food security gains increasing attention, automated pest monitoring is crucial for agricultural early warning systems. However, in practical light-trap capturing sensors, the extremely small scale of pests and complex background interference, such as unexpected reflection and occlusions, severely undermine the performance of existing models, resulting in frequent missed and false detections. To deal with these challenges, this study proposes EP-YOLO, an enhanced lightweight detection architecture based on YOLOv8n. Specifically, to retain the spatial pixels of micro-targets during downsampling and isolate pest features while eliminating background noise without compromising channel information, the Spatial-to-Depth Convolution (SPD) module and the Efficient Multi-Scale Attention (EMA) module are introduced. We evaluate our model through experiments on Pest24, a dataset consisting of 24 tiny pest categories. The results demonstrate that EP-YOLO achieves a mAP@50 and mAP@50:95 of 70.5% and 47.3%, respectively, improving upon the baseline by 1.1% and 1.9%. Furthermore, EP-YOLO achieves a significant improvement in detecting certain extremely small pests. For example, Rice planthopper and Plutella xylostella show improvements of 8.4% and 3.1%, respectively, compared to the baseline. In conclusion, the physical limitations of detecting tiny pests are successfully overcome by EP-YOLO, providing a robust and deployable design for real-time agricultural monitoring systems. Full article