ScanGuard-YOLO: Enhancing X-ray Prohibited Item Detection with Significant Performance Gains

Huang, Xianning; Zhang, Yaping

doi:10.3390/s24010102

Cited by 4 publications

(1 citation statement)

References 28 publications

(47 reference statements)

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“…It reaches a frame rate of 45 on VOC2007, significantly surpassing Faster R-CNN, which only achieves a frame rate of 7. This efficiency has led to its extensive application across various scenarios [17][18][19]. Regarding enhanced single-stage target detection algorithms tailored for specific tasks, refs.…”

Section: Related Workmentioning

confidence: 99%

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

Li,

Yuan,

Wang

et al. 2024

Applied Sciences

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In response to the challenges associated with the misassembly, omission, and low manual inspection efficiency in automobile wiring harness relay assemblies, a novel online detection system has been engineered. This system consists of a mobile-based visual imaging system and an improved YOLOv5-based detection algorithm that tracks human movement to acquire images and videos. The system is coupled with deep learning for real-time detection and recognition for error-proofing the installation process of automotive wiring harness relays. This innovation aims to facilitate error-proof inspection during the assembly process of automotive wiring harness relays. The YOLOv5s model is augmented with an Adaptive Spatial Feature Fusion (ASFF) module, enhancing multi-scale feature integration capabilities. A Global Context Network (GCNet) is incorporated into the C3 module to emphasize target information from a global perspective. Additionally, the replacement of standard Convolution (Conv) modules with Global Sparse Convolution (GSConv) modules in the Neck section effectively reduces computational costs while sustaining overall performance efficacy. The experimental results show that the detection system achieved a comprehensive accuracy rate of 99.2% and an F1 score of 99.29. The system possesses high accuracy and stability, enabling flexible and intelligent target detection applications in the automotive industry.

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Section: Related Workmentioning

confidence: 99%

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

Li,

Yuan,

Wang

et al. 2024

Applied Sciences

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Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Du,

Yu,

Wang

et al. 2024

Preprint

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This paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.

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Efficient X-ray Security Images for Dangerous Goods Detection Based on Improved YOLOv7

Liu,

Zhang,

et al. 2024

Electronics

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In response to the problems of complex background, multi-scale dangerous goods and severe stacking in X-ray security images, this paper proposes a high-accuracy dangerous goods detection algorithm for X-ray security images based on the improvement of YOLOv7. Firstly, by combining the coordinate attention mechanism, the downsampling structure of the backbone network is improved to enhance the model’s target feature localization ability. Secondly, a weighted bidirectional feature pyramid network is used as the feature fusion structure to achieve multi-scale feature weighted fusion and further simplify the network. Then, combined with dynamic snake convolution, a downsampling structure was designed to facilitate the extraction of features at different scales, providing richer feature representations. Finally, drawing inspiration from the idea of group convolution and combining it with Conv2Former, a feature extraction module called a multi-convolution transformer (MCT) was designed to enhance the network’s feature extraction ability by combining multi-scale information. The improved YOLOv7 in this article was tested on the public datasets SIXRay, CLCXray, and PIDray. The average detection accuracy (mAP) of the improved model was 96.3%, 79.3%, and 84.7%, respectively, which was 4.7%, 2.7%, and 3.1% higher than YOLOv7. This proves the effectiveness and universality of the method proposed in this article. Compared to the current mainstream X-ray image dangerous goods detection models, this model effectively reduces the false detection rate of dangerous goods in X-ray security inspection images and has achieved significant improvement in the detection of small and multi-scale targets, achieving higher accuracy in dangerous goods detection.

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ScanGuard-YOLO: Enhancing X-ray Prohibited Item Detection with Significant Performance Gains

Cited by 4 publications

References 28 publications

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Efficient X-ray Security Images for Dangerous Goods Detection Based on Improved YOLOv7

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