YOLOv5s-BC: an improved YOLOv5s-based method for real-time apple detection

Abstract

The current apple detection algorithms fail to accurately differentiate obscured apples from pickable ones, thus leading to low accuracy in apple harvesting and a high rate of instances where apples are either mispicked or missed altogether. To address the issues associated with the existing algorithms, this study proposes an improved YOLOv5s-based method, named YOLOv5s-BC, for real-time apple detection, in which a series of modifications have been introduced. First, a coordinate attention block has been incorporated into the backbone module to construct a new backbone network. Second, the original concatenation operation has been replaced with a bi-directional feature pyramid network in the neck network. Finally, a new detection head has been added to the head module, enabling the detection of smaller and more distant targets within the field of view of the robot. The proposed YOLOv5s-BC model was compared to several target detection algorithms, including YOLOv5s, YOLOv4, YOLOv3, SSD, Faster R-CNN (ResNet50), and Faster R-CNN (VGG), with significant improvements of 4.6%, 3.6%, 20.48%, 23.22%, 15.27%, and 15.59% in mAP, respectively. The detection accuracy of the proposed model is also greatly enhanced over the original YOLOv5s model. The model boasts an average detection speed of 0.018 s per image, and the weight size is only 16.7 Mb with 4.7 Mb smaller than that of YOLOv8s, meeting the real-time requirements for the picking robot. Furthermore, according to the heat map, our proposed model can focus more on and learn the high-level features of the target apples, and recognize the smaller target apples better than the original YOLOv5s model. Then, in other apple orchard tests, the model can detect the pickable apples in real time and correctly, illustrating a decent generalization ability. It is noted that our model can provide technical support for the apple harvesting robot in terms of real-time target detection and harvesting sequence planning.