Truck's head and tail recognition based on depthwise separable convolution modified Efficient-Net

Data collection with the mine vehicle monitoring system to all vehicles entering and leaving the mine, which contains a variety of vehicle pictures in different orientation, how to efficiently distinguish the truck’s head and tail is a major research challenge. Therefore, in this paper, we propose a head and tail recognition method based on CA attention improved Efficient-Net algorithm. We improve the accuracy of model feature extraction by introducing the coordinate attention mechanism (CA) to average the spatial feature information in two dimensions, X-axis and Y-axis, respectively, and changing the input and output of the residual edge of the inverse residual module to shorten the high-dimensional channel. Meanwhile, this paper introduces depth-separable convolution neural network and agent normalized activation in the mobile flip-flop convolution module to offset the different non-normalized sources of X-axis and Y-axis two dimensions between each convolution layer, and then improve the model detection rate. Combining the two methods, we achieve 99% val_Acc on the improved Efficient-Net network test set, which proves the algorithm is efficient.