Research on the Small Target Recognition Method of Automobile Tire Marking Points Based on Improved YOLOv5s

Abstract

At present, the identification of tire marking points relies primarily on manual inspection, which is not only time-consuming and labor-intensive but also prone to false detections, significantly impacting enterprise efficiency. To achieve accurate recognition of tire marking points, this study proposes a small target feature recognition method for automotive tire marking points. In image pre-processing, MSRCR (Multi-Scale Retinex with Color Restoration) is invoked to enhance image features, which can be adapted to different environmental detection tasks. The YOLOv5s network is improved by adding a parameter-free simAM (Similarity Attention Mechanism) attention mechanism to improve the detection efficiency; adding a small target prediction head in the network to improve the minimum recognition size of the network; and changing the loss function to improve the network recognition performance. MAP, precision, and recall are important parameters. The comparison experiment with the traditional YOLOv5s network shows that the mAP of the improved YOLOv5s network and the original network is 0.86 and 0.955, respectively, and the mAP is increased by 9.5%. The precision is 0.87 and 0.96, an improvement of 9%, and the recall rate is 0.84 and 0.89, an improvement of 4%; the improved YOLOv5s model has a higher confidence level for small target recognition and is more suitable for application in practical detection tasks.

Keywords

• deep learning
• YOLOv5 algorithm
• attention module
• target prediction head
• loss function
• tire identification point