Search Results (390)

Citrus Huanglongbing (HLB) is one of the most devastating diseases in the global citrus industry, but its early detection under complex field conditions remains a major challenge. Existing methods often suffer from insufficient dataset diversity and poor generalization, and struggle to accurately detect subtle early-stage lesions and multiple HLB symptoms in natural backgrounds. To address these issues, we propose an enhanced YOLO11-based framework, DCH-YOLO11. We constructed a multi-symptom HLB leaf dataset (MS-HLBD) containing 9219 annotated images across five classes: Healthy (1862), HLB blotchy mottling (2040), HLB Zinc deficiency (1988), HLB yellowing (1768), and Canker (1561), collected under diverse field conditions. To improve detection performance, the DCH-YOLO11 framework incorporates three novel modules: the C3k2 Dynamic Feature Fusion (C3k2_DFF) module, which enhances early and subtle lesion detection through dynamic feature fusion; the C2PSA Context Anchor Attention (C2PSA_CAA) module, which leverages context anchor attention to strengthen feature extraction in complex vein regions; and the High-efficiency Dynamic Feature Pyramid Network (HDFPN) module, which optimizes multi-scale feature interaction to boost detection accuracy across different object sizes. On the MS-HLBD dataset, DCH-YOLO11 achieved a precision of 91.6%, recall of 87.1%, F1-score of 89.3, and mAP50 of 93.1%, surpassing Faster R-CNN, SSD, RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n by 13.6%, 8.8%, 5.3%, 3.2%, 2.0%, 1.6%, 2.6%, 1.8%, and 1.6% in mAP50, respectively. On a publicly available citrus HLB dataset, DCH-YOLO11 achieved a precision of 82.7%, recall of 81.8%, F1-score of 82.2, and mAP50 of 89.4%, with mAP50 improvements of 8.9%, 4.0%, 3.8%, 3.2%, 4.7%, 3.2%, and 3.4% over RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n, respectively. These results demonstrate that DCH-YOLO11 achieves both state-of-the-art accuracy and excellent generalization, highlighting its strong potential for robust and practical citrus HLB detection in real-world applications. Full article

Strawberry (Fragaria × ananassa), a nutrient-dense fruit with significant economic value in commercial cultivation, faces critical detection challenges in automated harvesting due to complex growth conditions such as foliage occlusion and variable illumination. To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. Developed using a purpose-built dataset of 1021 annotated strawberry images (Fragaria × ananassa ‘Red Face’ and ‘Sachinoka’ varieties) from Changchun Xiaohongmao Plantation and augmented through targeted strategies to enhance model robustness, the framework demonstrates superior performance over existing lightweight detectors, achieving mAP50 improvements of 13.0%, 9.2%, and 3.9% against YOLOv7-tiny, YOLOv10n, and YOLOv11n, respectively. Remarkably, the architecture attains 96.4% mAP50 with only 1.3M parameters (57% reduction from baseline) and 4.4 GFLOPs (46% lower computation), simultaneously enhancing accuracy while significantly reducing resource requirements, thereby providing a robust technical foundation for automated ripeness assessment and precision harvesting in agricultural robotics. Full article

The rapid growth of the Internet of Things (IoT) and smart devices has led to an increasing demand for real-time data processing at the edge of networks closer to the source of data generation. This review paper introduces how artificial intelligence (AI) can be integrated with edge computing to enable efficient and scalable object recognition applications. It covers the key considerations of employing deep learning on edge computing devices, such as selecting edge devices, deep learning frameworks, lightweight deep learning models, hardware optimization, and performance metrics. An example of an application is also presented in this article, which is about real-time power transmission line detection using edge computing devices. The evaluation results show the significance of implementing lightweight models and model compression techniques such as quantized Tiny YOLOv7. It also shows the hardware performance on some edge devices, such as Raspberry Pi and Jetson platforms. Through practical examples, readers will gain insights into designing and implementing AI-powered edge solutions for various object recognition use cases, including smart surveillance, autonomous vehicles, and industrial automation. The review concludes by addressing emerging trends, such as federated learning and hardware accelerators, which are set to shape the future of AI on edge computing for object recognition. Full article

Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus ostreatus damage based on an improved YOLOv8n model, aiming to advance the accessibility of damage recognition technology, enhance automation in Pleurotus cultivation, and reduce labor dependency. This approach holds critical implications for agricultural modernization and serves as a pivotal step in advancing China’s agricultural modernization, while providing valuable references for subsequent research. Utilizing a self-collected, self-organized, and self-constructed dataset, we modified the feature extraction module of the original YOLOv8n by integrating a lightweight GhostHGNetv2 backbone network. During the feature fusion stage, the original YOLOv8 components were replaced with a lightweight SlimNeck network, and an Attentional Scale Sequence Fusion (ASF) mechanism was incorporated into the feature fusion architecture, resulting in the proposed OMB-YOLO model. This model achieves a remarkable balance between parameter efficiency and detection accuracy, attaining a parameter of 2.24 M and a mAP@0.5 of 90.11% on the test set. To further optimize model lightweighting, the DepGraph method was applied for pruning the OMB-YOLO model, yielding the OMB-YOLO-tiny variant. Experimental evaluations on the damaged Pleurotus dataset demonstrate that the OMB-YOLO-tiny model outperforms mainstream models in both accuracy and inference speed while reducing parameters by nearly half. With a parameter of 1.72 M and mAP@0.5 of 90.14%, the OMB-YOLO-tiny model emerges as an optimal solution for detecting Pleurotus ostreatus damage. These results validate its efficacy and practical applicability in agricultural quality control systems. Full article

To address the common issues of high small object miss rates, frequent false positives, and poor real-time performance in PCB defect detection, this paper proposes a multi-scale fusion algorithm based on the YOLOv12 framework. This algorithm integrates the Global Attention Mechanism (GAM) into the redesigned A2C2f module to enhance feature response strength of complex objects in symmetric regions through global context modeling, replacing conventional convolutions with hybrid weighted downsampling (HWD) modules that preserve copper foil textures in PCB images via hierarchical weight allocation. A bidirectional feature pyramid network (BiFPN) is constructed to reduce bounding box regression errors for micro-defects by fusing shallow localization and deep semantic features, employing a parallel perception attention (PPA) detection head combining dense anchor distribution and context-aware mechanisms to accurately identify tiny defects in high-density areas, and optimizing bounding box regression using a normalized Wasserstein distance (NWD) loss function to enhance overall detection accuracy. The experimental results on the public PCB dataset with symmetrically transformed samples demonstrate 85.3% recall rate and 90.4% mAP@50, with AP values for subtle defects like short circuit and spurious copper reaching 96.2% and 90.8%, respectively. Compared to the YOLOv12n, it shows an 8.7% enhancement in recall, a 5.8% increase in mAP@50, and gains of 16.7% and 11.5% in AP for the short circuit and spurious copper categories. Moreover, with an FPS of 72.8, it outperforms YOLOv5s, YOLOv8s, and YOLOv11n by 12.5%, 22.8%, and 5.7%, respectively, in speed. The improved algorithm meets the requirements for high-precision and real-time detection of multi-category PCB defects and provides an efficient solution for automated PCB quality inspection scenarios. Full article

Efficient and accurate license plate recognition (LPR) in unconstrained environments remains a critical challenge, particularly when confronted with skewed imaging angles and the limited computational capabilities of edge devices. In this study, we propose a high-performance, FPGA-based license plate alignment and recognition (LPAR) system to address these issues. Our LPAR system integrates lightweight deep learning models, including YOLOv4-tiny for license plate detection, a refined convolutional pose machine (CPM) for pose estimation and alignment, and a modified LPRNet for character recognition. By restructuring the pose estimation and alignment architectures to enhance the geometric correction of license plates and adding channel and spatial attention mechanisms to LPRNet for better character recognition, the proposed LPAR system improves recognition accuracy from 88.33% to 95.00%. The complete pipeline achieved a processing speed of 2.00 frames per second (FPS) on a resource-constrained FPGA platform, demonstrating its practical viability for real-time deployment in edge computing scenarios. Full article

The detection of tunnel cracks plays a vital role in ensuring structural integrity and driving safety. However, tunnel environments present significant challenges for crack detection, such as uneven lighting and shadow occlusion, which can obscure surface features and reduce detection accuracy. To address these challenges, this paper proposes a novel crack detection network named STCYOLO. First, a dynamic snake convolution (DSConv) mechanism is introduced to adaptively adjust the shape and size of convolutional kernels, allowing them to better align with the elongated and irregular geometry of cracks, thereby enhancing performance under challenging lighting conditions. To mitigate the impact of shadow occlusion, a Shadow Occlusion-Aware Attention (SOAA) module is designed to enhance the network’s ability to identify cracks hidden in shadowed regions. Additionally, a tiny crack upsampling (TCU) module is proposed, which reorganizes convolution kernels to more effectively preserve fine-grained spatial details during upsampling, thereby improving the detection of small and subtle cracks. The experimental results demonstrate that, compared to YOLOv8, our proposed method achieves a 2.85% improvement in mAP and a 3.02% increase in the F score on the crack detection dataset. Full article

Excessive bait wastage is a major issue in aquaculture, leading to higher farming costs, economic losses, and water pollution caused by bacterial growth from unremoved residual bait. To address this problem, we propose a bait residue detection and counting model named YOLOv8-BaitScan, based on an improved YOLO architecture. The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. The experimental results show that YOLOv8-BaitScan achieves strong performance across key metrics: The recall rate increased from 60.8% to 94.4%, mAP@50 rose from 80.1% to 97.1%, and the model’s number of parameters and computational load were reduced by 55.7% and 54.3%, respectively. The model significantly improves the accuracy and real-time detection capabilities for underwater bait and is more suitable for real-world aquaculture applications, providing technical support to achieve both economic and ecological benefits. Full article

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices. Full article

Due to the objects in UAV aerial images often presenting characteristics of multiple scales, small objects, complex backgrounds, etc., the performance of object detection using current models is not satisfactory. To address the above issues, this paper designs a multiscale small object detection model for UAV aerial images, namely MSUD-YOLO, based on YOLOv10s. First, the model uses an attention scale sequence fusion mode to achieve more efficient multiscale feature fusion. Meanwhile, a tiny prediction head is incorporated to make the model focus on the low-level features, thus improving its ability to detect small objects. Secondly, a novel feature extraction module named CFormerCGLU has been designed, which improves feature extraction capability in a lighter way. In addition, the model uses lightweight convolution instead of standard convolution to reduce the model’s computation. Finally, the WIoU v3 loss function is used to make the model more focused on low-quality examples, thereby improving the model’s object localization ability. Experimental results on the VisDrone2019 dataset show that MSUD-YOLO improves mAP50 by 8.5% compared with YOLOv10s. Concurrently, the overall model reduces parameters by 6.3%, verifying the model’s effectiveness for object detection in UAV aerial images in complex environments. Furthermore, compared with multiple latest UAV object detection algorithms, our designed MSUD-YOLO offers higher detection accuracy and lower computational cost; e.g., mAP50 reaches 43.4%, but parameters are only 6.766 M. Full article

To address the challenges of detecting spray code defects caused by complex morphological variations and the discrete characterization of dot-matrix spray codes, an improved YOLOv7-tiny algorithm named DASS-YOLO is proposed. Firstly, the DySnakeConv module is employed in Backbone–Neck cross-layer connections. With a dynamic structure and adaptive learning, it can capture the complex morphological features of spray codes. Secondly, we proposed an Attention-guided Shape Enhancement Module with CAA (ASEM-CAA), which adopts a symmetrical dual-branch structure to facilitate bidirectional interaction between local and global features, enabling precise prediction of the overall spray code shape. It also reduces feature discontinuity in dot-matrix codes, ensuring a more coherent representation. Furthermore, Slim-neck, which is famous for its more lightweight structure, is adopted in the Neck to reduce model complexity while maintaining accuracy. Finally, Shape-IoU is applied to improve the accuracy of the bounding box regression. Experiments show that DASS-YOLO improves the detection accuracy by 1.9%. Additionally, for small defects such as incomplete code and code spot, the method achieves better accuracy improvements of 8.7% and 2.1%, respectively. Full article

Autonomous vehicles are one of the key components of smart mobility that leverage innovative technology to navigate and operate safely in urban environments. Traffic light detection systems, as a key part of autonomous vehicles, play a key role in navigation during challenging traffic scenarios. Nighttime driving poses significant challenges for autonomous vehicle navigation, particularly in regard to the accuracy of traffic lights detection (TLD) systems. Existing TLD methodologies frequently encounter difficulties under low-light conditions due to factors such as variable illumination, occlusion, and the presence of distracting light sources. Moreover, most of the recent works only focused on daytime scenarios, often overlooking the significantly increased risk and complexity associated with nighttime driving. To address these critical issues, this paper introduces a novel approach for nighttime traffic light detection using the LNT-YOLO model, which is based on the YOLOv7-tiny framework. LNT-YOLO incorporates enhancements specifically designed to improve the detection of small and poorly illuminated traffic signals. Low-level feature information is utilized to extract the small-object features that have been missing because of the structure of the pyramid structure in the YOLOv7-tiny neck component. A novel SEAM attention module is proposed to refine the features that represent both the spatial and channel information by leveraging the features from the Simple Attention Module (SimAM) and Efficient Channel Attention (ECA) mechanism. The HSM-EIoU loss function is also proposed to accurately detect a small traffic light by amplifying the loss for hard-sample objects. In response to the limited availability of datasets for nighttime traffic light detection, this paper also presents the TN-TLD dataset. This newly curated dataset comprises carefully annotated images from real-world nighttime driving scenarios, featuring both circular and arrow traffic signals. Experimental results demonstrate that the proposed model achieves high accuracy in recognizing traffic lights in the TN-TLD dataset and in the publicly available LISA dataset. The LNT-YOLO model outperforms the original YOLOv7-tiny model and other state-of-the-art object detection models in mAP performance by 13.7% to 26.2% on the TN-TLD dataset and by 9.5% to 24.5% on the LISA dataset. These results underscore the model’s feasibility and robustness compared to other state-of-the-art object detection models. The source code and dataset will be available through the GitHub repository. Full article

In intelligent building, efficient surface defect detection is crucial for structural safety and maintenance quality. Traditional methods face three challenges in complex scenarios: locating defect features accurately due to multi-scale texture and background interference, missing fine cracks because of their tiny size and low contrast, and the insufficient generalization of irregular defects due to complex geometric deformation. To address these issues, an improved version of the You Only Look Once (YOLOv8) algorithm is proposed for building surface defect detection. The dataset used in this study contains six common building surface defects, and the images are captured in diverse scenarios with different lighting conditions, building structures, and ages of material. Methodologically, the first step involves a normalization-based attention module (NAM). This module minimizes irrelevant features and redundant information and enhances the salient feature expression of cracks, delamination, and other defects, improving feature utilization. Second, for bottlenecks in fine crack detection, an explicit vision center (EVC) feature fusion module is introduced. It focuses on integrating specific details and overall context, improving the model’s effectiveness. Finally, the backbone network integrates deformable convolution net v2 (DCNV2) to capture the contour deformation features of targets like mesh cracks and spalling. Our experimental results indicate that the improved model outperforms YOLOv8, achieving a 3.9% higher mAP50 and a 4.2% better mAP50-95. Its performance reaches 156 FPS, suitable for real-time inspection in smart construction scenarios. Our model significantly improves defect detection accuracy and robustness in complex scenarios. The study offers a reliable solution for accurate multi-type defect detection on building surfaces. Full article

Accurate detection of surface defects on passion fruits is crucial for maintaining market competitiveness. Numerous small defects present significant challenges for manual inspection. Recently, deep learning (DL) has been widely applied to object detection. In this study, a lightweight neural network, StarC3SE-CBAM-DIoU-YOLOv5s (SCD-YOLOv5s), is proposed based on YOLOv5s for real-time detection of tiny surface defects on passion fruits. Key improvements are introduced as follows: the original C3 module in the backbone is replaced by the enhanced StarC3SE module to achieve a more efficient network structure; the CBAM module is integrated into the neck to improve the extraction of small defect features; and the CIoU loss function is substituted with DIoU-NMS to accelerate convergence and enhance detection accuracy. Experimental results show that SCD-YOLOv5s performs better than YOLOv5s, with precision increased by 13.2%, recall by 1.6%, and F₁-score by 17.0%. Additionally, improvements of 6.7% in mAP@0.5 and 5.5% in mAP@0.95 are observed. Compared with manual detection, the proposed model enhances detection efficiency by reducing errors caused by subjective judgment. It also achieves faster inference speed (26.66 FPS), and reductions of 9.6% in parameters and 8.6% in weight size, while maintaining high detection performance. These results indicate that SCD-YOLOv5s is effective for defect detection in agricultural applications. Full article

The accurate identification of look-alike medical vials is essential for patient safety, particularly when similar vials contain different substances, volumes, or concentrations. Traditional methods, such as manual selection or barcode-based identification, are prone to human error or face reliability issues under varying lighting conditions. This study addresses these challenges by introducing a real-time deep learning-based vial identification system, leveraging a Lightweight YOLOv4 model optimized for edge devices. The system is integrated into a Mixed Reality (MR) environment, enabling the real-time detection and annotation of vials with immediate operator feedback. Compared to standard barcode-based methods and the baseline YOLOv4-Tiny model, the proposed approach improves identification accuracy while maintaining low computational overhead. The experimental evaluations demonstrate a mean average precision (mAP) of 98.76 percent, with an inference speed of 68 milliseconds per frame on HoloLens 2, achieving real-time performance. The results highlight the model’s robustness in diverse lighting conditions and its ability to mitigate misclassifications of visually similar vials. By combining deep learning with MR, this system offers a more reliable and efficient alternative for pharmaceutical and medical applications, paving the way for AI-driven MR-assisted workflows in critical healthcare environments. Full article