Apple object detection based on improved YOLOX

Abstract

To enhance the working efficiency of apple picking systems by improving the accuracy of object detection in natural scenes, an improved apple detection network based on YOLOX-s is proposed. The self-attention residual module is added to the last layer of the improved YOLOX-s, which is used to add global feature information for small apples. An additional object detection head is added in YOLOX-s to strengthen the feature information of dense objects. Extra convolutional branches are added into the spatial pyramid pooling structure to enhance feature fusion and compensate for lost object location information. The loss function of the network is replaced by the α-CIoU loss, which is used to improve the bbox regression accuracy by up-weighting the loss and gradient of the high IoU prediction box. Experiment results show that the mAP50 value and the recall rate of the improved network reached 91.8% and 97%, respectively. Therefore, the improved network is superior to the existing detection networks in detection accuracy. Meanwhile, the detection time increased slightly, but it still meets the requirements of real-time detection of the picking system.