Research on Intelligent Detection Models for Automotive Fuel Caps in Autonomous Refueling Systems under Complex Scenarios

In unmanned intelligent refueling systems, complex scenarios such as dirty vehicle bodies, low light, and improper angles often directly affect the detection of fuel tank lids. This paper proposes a comprehensive improved model, BES-YOLO, based on the YOLOv8 framework. First, three instances of the BiFPN multi-layer feature extraction network were added to the architecture to enhance the detection accuracy of small objects. Second, an effective attention mechanism module (EMA) was introduced, adding EMA modules to each output layer of the detection head to strengthen feature representation. Third, the SIOU loss function was adopted to optimize the distance and relative angle between predicted and ground truth boxes. Experimental results show that compared to YOLOv8n, the BES-YOLO model achieved an accuracy of 98.2%, mAP@50 of 99.2%, and mAP@50:95 of 71.9% on the self-built dataset, with increases of 0.2%, 0.2%, and 1.1%, respectively. Testing on the VOC dataset yielded results of 75.6%, 74.0%, and 51.8%, representing increases of 5.4%, 1.6%, and 1.2%, respectively, demonstrating significant detection performance.