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Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model.The method first introduces a lightweight feature extraction structure, E-GhostV2 network, into the trunk and neck of the model to achieve effective feature extraction while reducing the burden of the model. Additionally, the PConv is used in the model's backbone to effectively reduce redundant computations and memory access, thereby enhancing efficiency and feature extraction capabilities. Furthermore, to address the problem of performance degradation caused by overemphasizing high-quality samples, the model adopts the WIoU loss function, which balances the training effect of high-quality and low-quality samples, enhancing the model's robustness and generalization performance. Experimental results demonstrate that the improved model achieves satisfactory detection accuracy while reducing the number of model parameters, providing robust support for theoretical research and practical applications in the field of hazardous chemical vehicle object detection., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image's grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model's perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model's FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model.