Abstract
In the mining operation scene, accidents caused by dangerous driving behaviors of drivers occur frequently. Therefore, the driver’s behavior detection can provide early warning and reduce the incidence of accidents. The driver behavior detection task has the issue of high false detection rate of small target detection because of the eyes, mouth and cigarette. Therefore, we propose a new model YOLO-BS, which uses a new structure EVITS and ASPPMP. The EVITS module captures the local and global information of the image feature map, and performs information shuffle and weighted aggregation by grouping and random shift. The purpose is to make the information between groups can spread and interact with each other, and further capture the global context information. The ASPPMP structure has two main functions. One is to use the serialized dilated convolution pyramid to obtain information of different receptive fields and extract multi-scale feature representations. The second is to use the serialized maximum pooling structure to aggregate the local information of the image. By considering the pixels, regions and features around the target, the relationship between the target and its surrounding environment is captured, which helps the algorithm to locate and classify the target more accurately. Experiments on the self-built driver behavior dataset show that the mAP of YOLO-BS is 1.8% higher than that of YOLOv8s in accuracy. In terms of speed, YOLO-BS and YOLOv8s are similar. Our model has been deployed on hundreds of work vehicles at a local mining site to help warn of dangerous driving and reduce the likelihood of accidents.
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Acknowledgments
This work was supported in part by Inner Mongolia Natural Science Foundation of China under Grant No. 2021MS06016; Research Project on Strengthening the Construction of Important Ecological Security Barrier in Northern China by Higher Education Institutions in Inner Mongolia Autonomous Region under Grant No. STAQZX202321; Self-project of the Engineering Research Center of Ecological Big Data, Ministry of Education, China.
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Xi, Y., Guo, J., Ma, M. (2024). YOLO-BS: A Better Object Detection Model for Real-Time Driver Behavior Detection. In: Huang, DS., Pan, Y., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14873. Springer, Singapore. https://doi.org/10.1007/978-981-97-5615-5_7
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