Target detection technology of coal mine wheeled robot combining improved CNN and self attention mechanism

In complex coal mine environment and poor lighting conditions, the existing target detection technology is difficult to meet the needs of intelligent inspection. To solve this problem, an object detection method based on improved convolutional neural network and self-attention mechanism is proposed. Firstly, a feature extraction network based on pyramid structure and attention mechanism is constructed. On this basis, the bidirectional feature pyramid network module is designed to further strengthen the feature extraction function. Finally, the YOLO Head module is used for prediction processing. The test results show that after 2 398 iterations, the loss of the model is stabilized at about 0.01, and the ideal loss effect is achieved. The accuracy of the model reached 0.95 at 1 598 iterations and entered steady state at about 1 845 iterations, which was one of the fastest models to reach steady state. The overall detection time was 3.2 ms. The model can improve the accuracy and efficiency of target detection in complex environment.