Search Results (475)

Object detection in unstructured agricultural environments remains challenging due to large scale variations, complex backgrounds, irregular obstacle shapes, and limited computational resources. To address these challenges, this paper proposes Agri-DETR, an efficient end-to-end detection framework based on the Real-Time Detection Transformer (RT-DETR), with coordinated improvements in feature perception, multi-scale representation, spatial reconstruction, and bounding box regression. Specifically, a lightweight backbone with a high-resolution feature branch is introduced to enhance the representation of small and fine-grained targets. A large selective feature fusion module is designed to strengthen multi-scale contextual modeling and improve feature discrimination under complex backgrounds. In addition, an attention-enhanced dynamic upsampling module refines high-resolution feature reconstruction, while a scale–shape–geometry-aware Intersection over Union (SSGIoU) loss improves localization stability for irregular and elongated objects. Experimental results show that Agri-DETR achieves 66.0% Average Precision (AP) on the self-constructed Agricultural Obstacle Dataset (AO-Dataset), outperforming representative detectors while reducing the parameter count by approximately 25% compared with RT-DETR-R18 baseline. In particular, small-object AP increases by 1.4%, demonstrating improved detection capability for small obstacles. Cross-dataset evaluation on COCO2017 further shows that Agri-DETR achieves 48.3% AP, demonstrating favorable generalization capability beyond the agricultural domain. These results indicate that Agri-DETR achieves an effective balance among detection accuracy, model complexity, and practical efficiency, making it a promising solution for real-world agricultural obstacle detection. Full article

Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening. Full article

Crack detection in mobile inspection scenarios is constrained by both the extremely slender geometry of crack targets and the real-time inference requirements on edge devices, which expose systematic limitations of general-purpose object detectors. This paper proposes YOLO-Crack, a closed-loop solution that couples geometry-statistics-driven module design with end-to-end edge deployment validation. On the algorithmic side, we first quantify crack geometric properties and then introduce (i) a crack-aware cross-dimensional fusion attention (CFCA) module to strengthen feature representations, (ii) a dual-path feature enhancement module (DFEM) to preserve fine details during upsampling, and (iii) an empirical smooth quality window adjustment with shape consistency regularization to stabilize bounding-box regression for slender cracks. Experiments on the Crack500 dataset show that YOLO-Crack achieves 78.8% precision, 51.4% recall, and 65.7% mAP@0.5, improving over the YOLOv11n baseline by 4.2, 1.7, and 2.9 percentage points, respectively. On the engineering side, we deploy YOLO-Crack on a Jetson Orin NX mobile robot platform and evaluate it in a real ROS pipeline; the measured end-to-end throughput reaches 25.5 FPS, meeting real-time video processing requirements. The proposed framework provides a practical reference workflow for edge vision tasks, from geometry analysis to engineering verification. Full article

Real-time perception of coal–biomass blending during conveyor-belt transport remains challenging because of local aggregation, particle overlap, and illumination variation. In this study, a laboratory-scale conveyor-belt image dataset covering different coal mass fractions, illumination conditions, and particle sizes was constructed. Whole-image classification, cropped-ROI classification, direct regression, horizontal bounding box (HBB)-based detection, oriented bounding box (OBB)-based detection, and RT-DETR-L detection baselines were compared using YOLO-series and auxiliary models. Coal mass fraction was estimated using a frequency-weighted statistical strategy that converts frame-level predictions into continuous estimates. YOLOv8-cls achieved an average RMSE of 13.98 percentage points (pp), indicating the influence of background interference in whole-image classification. Among HBB models, YOLOv8m achieved the lowest mean RMSE of 6.10 pp but required higher computational cost. Compared with YOLOv8n, YOLOv8n-OBB reduced the average RMSE from 9.02 to 6.90 pp by providing a more compact material-region representation and reducing background redundancy. These results show that OBB representation improves the stability of lightweight models. The proposed method provides a feasible vision-based soft-sensing approach for online trend monitoring of coal–biomass blending under lightweight deployment. Full article

Fish observation is a key component of marine ecological monitoring and is valuable for understanding ecological processes and fish population dynamics. In practical applications, observation equipment is often constrained by limited memory and computational resources, making it difficult to deploy visual detection models with large parameter counts and high computational complexity. Under limited computational resources, existing deep-learning-based fish detection models struggle to balance detection accuracy, model lightweighting, and real-time edge deployment. To address this issue, a lightweight GEM-YOLOv8n model based on YOLOv8n is proposed for fish detection. For high model complexity, insufficient feature representation, and limited bounding box regression accuracy in complex underwater observation scenarios, the model replaces C2f modules and some Conv modules with GhostC2f and GhostConv, introduces the EMA attention mechanism, and adopts MPDIoU loss instead of CIoU. Experimental results show that, compared with YOLOv8n, GEM-YOLOv8n improves Precision, Recall, mAP50, and mAP50–95 by 0.53%, 2.16%, 0.52%, and 0.34% while reducing parameters and FLOPs by 48.0% and 39.5%. These results demonstrate that the proposed model improves detection performance while reducing model complexity. Tests on Jetson Xavier NX demonstrate real-time performance and deployment feasibility, providing a lightweight deployment solution for resource-constrained underwater fish detection. Full article

Object detection in UAV remote sensing imagery remains challenging due to severe scale variation, dense object distributions, complex background clutter, and localization ambiguity caused by extremely small objects. To address these issues, this paper proposes BDRNet, a lightweight background-aware dynamic-scale routing network for UAV remote sensing object detection. First, a background-aware feature enhancement (BAFE) module is introduced into the backbone to enhance feature representation through horizontal and vertical contextual modeling, improving target-related responses in complex aerial scenes. Second, a dynamic-scale routing pyramid (DSRP) is designed to retain the high-resolution $P_2$ branch and adaptively integrate multi-scale features through spatially dynamic routing, alleviating the loss of fine-grained information and improving the representation of small and scale-varied objects. Third, a scale- and geometry-aware normalized Wasserstein distance (SGNW) loss is proposed by modeling bounding boxes as two-dimensional Gaussian distributions. By incorporating aspect-ratio-guided geometric weighting and scale-aware dynamic fusion, SGNW improves regression stability for small objects while preserving geometric constraints for medium and large targets. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that BDRNet consistently improves detection accuracy over the YOLOv10s detector while maintaining a comparable model size and computational cost. Compared with several mainstream lightweight detectors, BDRNet achieves a favorable accuracy–efficiency trade-off, demonstrating its effectiveness for UAV remote sensing object detection in complex aerial scenarios. Full article

Aim: This study aimed to detect the maxillary dental midline region on orthodontic frontal photographs using a YOLOv8-based deep learning approach and to evaluate how the detection outputs affect the classification performance of various machine learning algorithms in distinguishing symmetric from asymmetric midline conditions. Materials and Methods: A total of 146 standardized frontal photographs (72 with midline deviation ≥ 2 mm from the facial midline, defined by the soft-tissue nasion–subnasal line; 74 symmetric) were analyzed. YOLOv8 was used to obtain bounding-box and keypoint predictions, which were converted into a numerical feature vector and used to train 11 classifiers (including Naive Bayes, Logistic Regression with L1 and ElasticNet penalties, Support Vector Machine, AdaBoost, and others). Performance was assessed using accuracy (with 95% Wilson confidence intervals), precision, recall, F1-score, and ROC-AUC. Optimization of hyperparameters for the downstream classifiers employed five-fold cross-validation along with grid search inside the training data set (n = 126) while final classifier assessment was done using a reserved test data set (n = 20). As the YOLOv8 object detector was trained using the full image dataset before extracting features, the classification metrics presented here should be considered as exploratory results only. Results: YOLOv8 achieved mAP@0.5 = 0.995 for midline detection. Naive Bayes attained the highest classification accuracy of 75% (95% CI: 53–89%) with ROC-AUC = 0.75. AdaBoost achieved 65% (95% CI: 43–82%). Several models defaulted to majority-class prediction (accuracy = 40%), indicating insufficient feature discriminability. Conclusions: YOLOv8 detected the maxillary dental midline under the present internal experimental conditions. However, because leakage-free outer k-fold validation of the complete detection-plus-classification pipeline was not performed, the classification results should be considered preliminary. Future work should address information leakage, incorporate facial reference frame normalization, include inter-observer reliability assessment, and validate the approach on larger datasets. Full article

To address missed detections, false alarms, and deployment limitations in thermal defect detection of photovoltaic modules from unmanned aerial vehicle (UAV) infrared images, this paper proposes an improved detection method based on You Only Look Once version 8 nano (YOLOv8n). The proposed method is optimized according to the characteristics of UAV infrared photovoltaic inspection, including small thermal targets, weak and diffuse thermal responses, complex backgrounds, and lightweight deployment requirements. Specifically, a P2 shallow feature layer is introduced to enhance fine-grained feature perception for small thermal defects, while Ghost Convolution (GhostConv) is incorporated into the backbone to reduce model complexity. In addition, C2f-Large Separable Kernel Attention (C2f-LSKA) is embedded in the neck to strengthen contextual and spatial feature modeling under complex infrared backgrounds, and Wise-IoU version 3 (WIoUv3) is adopted to improve bounding box regression and localization stability for boundary-ambiguous thermal anomalies. Experiments are conducted on a self-constructed UAV infrared thermal imaging dataset. From nearly 10,000 inspection images, 3000 representative images are selected and manually annotated, covering typical challenges such as small hot spots, low-contrast defects, complex background interference, and diffuse abnormal temperature-rise regions. Compared with the baseline YOLOv8n, the proposed method improves Precision, Recall, mean average precision at an IoU threshold of 0.5 (mAP@0.5), and mean average precision averaged over IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) by 5.1, 11.4, 9.6, and 13.2 percentage points, respectively, while reducing the number of parameters and model size by 65.8% and 61.9%, respectively. These results indicate that the proposed method improves detection accuracy and localization quality under the evaluated UAV infrared inspection setting while maintaining lightweight characteristics. Full article

Aircraft targets in SAR imagery often exhibit discrete scattering characteristics, significant variations in pose and scale, strong speckle noise in background clutter, and complex background interference, which jointly hinder stable structural feature extraction and accurate target localization. Existing detectors for SAR aircraft recognition primarily rely on bounding-box regression and classification; they do not completely exploit target structural cues, spatial attention, and frequency-domain information. To address these limitations, we propose a collaborative detection framework that integrates an uncertainty-aware keypoint-driven module (UAKM) with a fractional Fourier convolution backbone (S-FRConv). UAKM introduces a center-keypoint regression branch that jointly predicts keypoint coordinates and Laplacian scale parameters and employs a 2D Laplace negative log-likelihood loss to estimate uncertainty. The derived dense uncertainty heatmap is then used as spatial attention weights to guide distribution-based regression and multi-scale feature re-weighting, without requiring any additional annotations. S-FRConv embeds the Fractional Fourier Transform into shallow backbone layers and C2f modules, enabling joint spatial–spectral feature modeling that suppresses speckle noise and enhances edge and orientation representations. Experiments on the public SAR-AIRcraft-1.0 dataset demonstrate that the proposed method systematically improves the detection performance. For the Nano model, the overall mAP50 increases from 0.810 to 0.867, and the mAP 50:95 improves from 0.637 to 0.655 compared with the baseline, corresponding to gains of 5.7 and 1.8 percentage points, respectively. These results validate the effectiveness and generalization potential of combining uncertainty-driven spatial attention with fractional spectral feature enhancement for SAR aircraft target detection. Full article

This study was conducted to address the challenges of detecting citrus fruits in complex orchard environments characterized by overlap, occlusion, and variable lighting conditions. To tackle these issues, an improved detection model named YOLO-MGP was developed based on the YOLOv8n architecture. Four key enhancements were introduced to the core components of the detection framework. First, the primary backbone network was replaced with MobileNetV3, which substantially reduced computational requirements while preserving the capability for multi-scale feature extraction. Second, a C2f-GLU module was incorporated into the neck network. By leveraging Gated Linear Units, this module strengthens the feature selection and fusion processes. Third, an additional P2 detection layer was added to improve the detection of small targets. This modification was complemented by the integration of a Coordinate Attention mechanism, which refines the distribution of feature weights across spatial and channel dimensions. Finally, the CIoU loss was replaced by PIoU to enhance the accuracy of bounding box regression, particularly for occluded and overlapping targets. Experimental results demonstrate that the YOLO-MGP model achieved a precision of 94.2%, a recall of 89.7%, and a $mA{P}_{50}$ of 95.7% on our custom citrus dataset. By substantially reducing the number of parameters while maintaining competitive detection performance, the proposed method offers a practical and lightweight solution for fruit detection in automated harvesting systems. Full article

To reduce confusion between adjacent maturity categories, as well as false detections and low detection accuracy caused by complex backgrounds in tomato object detection, this study develops an improved YOLOv7-based model, named YOLO-RCM (Reduce classes misjudgment). First, a stability-enhanced ECANet channel attention module is embedded into the feature pyramid network (FPN) to strengthen discriminative channel responses. Second, a DCNv2-based deformable convolution enhancement module, namely DCNConv with adaptive magnitude constraints, is incorporated into the backbone network to alleviate feature misalignment caused by shape variation, partial occlusion, and fine-grained appearance differences in tomato maturity detection. Third, the WIoU v3 loss function is adopted to refine bounding box regression stability. The model was evaluated on the public Laboro Tomato dataset and TomatOD dataset. Experimental results indicate that YOLO-RCM obtains 83.7% Precision and 89.6% mAP@0.5, exceeding the baseline by 3.3 and 1.2 percentage points, respectively. Its Recall is 80.5%, with a decrease of 0.8 percentage points, whereas GFLOPs are reduced to 96.9, 6.3 lower than the baseline. These results indicate that the proposed method improves detection accuracy and computational efficiency while maintaining an almost unchanged model scale. The confusion matrix and PR curves further show that YOLO-RCM can effectively mitigate misdetections associated with adjacent maturity stages and complex scenes. In the external-dataset robustness test, Precision and mAP@0.5 are improved by 5.8 and 4.0 percentage points over the baseline, respectively, confirming the generalization ability of the proposed model. The main contribution of this study lies in improving tomato maturity detection from three complementary aspects: channel feature discrimination, local geometric perception, and bounding box regression stability. The study offers a practical technical reference for intelligent tomato harvesting systems in complex agricultural environments. Full article

To mitigate the drawbacks of joint crossover (IoU) in complex detection scenarios, this paper proposes a normalized IoU strategy. This strategy enhances the matching robustness in multi-scale object detection by introducing target scale parameters. The proposed method shows comparable or superior average precision (mAP) performance to traditional methods on public datasets. In addition, we have designed a dual-center distance penalty mechanism that implicitly enforces symmetric constraints between bounding boxes, increasing the number of positive samples detected. Our method has been evaluated on mainstream public datasets and unmanned aerial vehicle (UAV) water level gauge datasets, as well as evaluated using the You Only Look Once (YOLO) framework. Our method increased the average number of positive samples by 2.28% compared to CIoU. It also surpasses the most advanced technology. The dual-center constraint enhances the spatial alignment between bounding boxes. This results in notable performance gains in challenging scenarios. These scenarios involve blurred and heavily occluded objects. After parameter optimization, the proposed method achieves significant accuracy improvements. These improvements are seen in detecting small-scale and occluded characters. Full article

Despite the ocean’s vast energy reserves and extensive coverage of Earth’s surface, the complexity of the underwater environment has hindered effective target recognition. To mitigate the feature degradation caused by underwater scattering, wavelength-dependent absorption, and non-uniform illumination, this study proposes a degradation-aware YOLOv11s-based detection framework for underwater robotic object detection. The framework enhances detection robustness by improving feature reconstruction, channel–spatial attention, and bounding-box regression in a unified architecture. First, to address limitations in model parameters, spatial channel reconstruction convolutions (SCConv) replace certain traditional convolutions. Sequential reconstruction via SRU-CRU effectively suppresses spatial and channel redundancy, enabling a more precise capture of underwater target deformations and complex features. Second, the Shuffle Attention module enhances the interaction between channel and spatial features, improving the model’s fine-grained representation of underwater targets, highlighting granular objects and key textures. Finally, Focaler-IoU is employed to linearly remap the IoU interval, improving the accuracy and convergence stability of bounding-box regression. These components work together to improve the model’s robustness in degraded underwater scenes. In the underwater robotic detection task, the improved model achieves an mAP@0.5 of 88.4%, which is 3.2 percentage points higher than that of the baseline YOLOv11s. These results indicate that the proposed model improves detection accuracy while maintaining the real-time requirements of underwater robotic applications. Full article

Stainless steel pipes are critical components in industrial systems such as oil and gas transportation and nuclear power cooling. Surface defects can severely degrade their mechanical performance and operational safety. However, existing inspection methods still face challenges including difficult feature extraction, strong reflection interference, and limited accuracy in small-target detection. To address these issues, this paper proposes an improved detection algorithm termed YOLOv8s-BISW (incorporating BiFPN, SGE attention, and WIoU loss), which introduces multidimensional optimizations based on the YOLOv8s baseline. First, an image enhancement module combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE) is designed to mitigate uneven illumination and blurred defect imaging. Second, a Bidirectional Feature Pyramid Network (BiFPN) structure is introduced to strengthen multi-scale feature fusion and improve adaptability to defects of different sizes. Meanwhile, a Spatial Group-wise Enhance (SGE) attention module is embedded into the backbone to enhance defect feature representation while suppressing background interference. Furthermore, the Wise Intersection over Union (WIoU) loss function replaces Complete IoU (CIoU) to improve bounding box regression for irregular defects. Experimental results show that the proposed model achieves an mAP of 0.979 on a self-constructed Stainless-steel Tube Flaw (STF) dataset. Compared with the original YOLOv8s, precision, recall, and mAP are improved by 0.007, 0.010, and 0.033, respectively, while the average detection time per image is only 3.7 ms, achieving a favorable balance between accuracy and real-time performance. Compared with mainstream algorithms such as SSD, YOLOv3, and Faster R-CNN, the proposed method demonstrates superior overall performance, providing reliable technical support for automated surface defect detection of stainless steel pipes and offering practical value for intelligent manufacturing quality control. Full article

Currently, devices such as solar insecticidal lamps are widely used in agricultural pest control, but routine trapping is insufficient to meet the demands of precision agriculture. Therefore, determining the nocturnal phototactic rhythm of pests to optimize the control strategies of insecticidal lamps has become key to achieving precise pest control. However, existing automated monitoring and forecasting devices struggle to effectively monitor the nocturnal phototactic rhythm of small pests. To address this issue, this study developed an automated monitoring system for phototactic rhythm based on sticky traps and machine vision. For the hardware, an image acquisition device integrating a darkroom and scheduled supplementary lighting was designed to obtain stable time-series images of nocturnal pests. For the algorithm, the YOLO-STP detection model was proposed by improving upon the baseline YOLOv11 model. This model introduces a P2 detection layer, a Coordinate Attention (CA) mechanism, and a hybrid bounding box regression loss function integrating WIoU and NWD. Combined with a sliding window cropping method, it further enhances the detection capability for small objects. Additionally, an incremental counting method based on spatial cascade matching was proposed to mitigate counting errors caused by target occlusion or detachment in the time-series images. Experimental results indicate that the mean average precision (mAP) of the detection model was 93.2%. For the counting method, the coefficient of determination ($R^2$) was 0.98, with an RMSE of 1.97 and an MAE of 1.60. Field validation in real-world paddy fields demonstrated that the system can accurately record the abundance changes of 12 pest species, intuitively visualizing the differences in phototactic rhythms among various species. This study provides a viable automated monitoring tool for acquiring the nocturnal activity rhythm data of agricultural pests in the field. Full article