Affiliation:
1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2. School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
Abstract
Engineering vehicles play a vital role in supporting construction projects. However, due to their substantial size, heavy tonnage, and significant blind spots while in motion, they present a potential threat to road maintenance, pedestrian safety, and the well-being of other vehicles. Hence, monitoring engineering vehicles holds considerable importance. This paper introduces an engineering vehicle detection model based on improved YOLOv6. First, a Swin Transformer is employed for feature extraction, capturing comprehensive image features to improve the detection capability of incomplete objects. Subsequently, the SimMIM self-supervised training paradigm is implemented to address challenges related to insufficient data and high labeling costs. Experimental results demonstrate the model’s superior performance, with a mAP50:95 value of 88.5% and mAP50 value of 95.9% on the dataset of four types of engineering vehicles, surpassing existing mainstream models and proving its effectiveness in engineering vehicle detection.
Funder
The National Natural Science Foundation of China
Science and Technology Project of Xuzhou
Postgraduate Research & Practice Innovation Program of Jiangsu Province
Graduate Innovation Program of China University of Mining and Technology
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