Detection of secondary school circuit experiment equipment based on improved YOLOX

Abstract

Aiming at the problem that it is difficult for the existing target detection algorithms to detect high-precision circuit experimental equipment in middle school, an improved YOLOX detection network model is proposed. Based on the YOLOX network model. Firstly, the ECA attention module is added to the feature extraction network to enhance the model's ability to perceive electrical experimental equipment; Secondly, the feature enhancement structure is added to enhance the semantic information of the obtained feature map and improve the detection ability of the target; Finally, EIoU is selected as the loss function to achieve high-precision positioning. The experimental results show that the improved network model mAP reaches 91.9%, which is 1.5% higher than the original network model, which proves that the improvement is effective and feasible.