Bunet: An effective and efficient segmentation method based on bilateral encoder-decoder structure for rapid detection of apple tree branches

Automatic apple harvesting robots have received much research attention in recent years to lower harvesting costs. A fundamental problem for harvesting robots is how to quickly and accurately detect branches to avoid collisions with limited hardware resources. In this paper, we propose a lightweight, high-accurate and real-time semantic segmentation network, Bilateral U-shape Network (BUNet), to segment apple tree branches. The BUNet consists mainly of a U-shaped detail branch and a U-shaped semantic branch, the former for capturing spatial details and the latter for supplementing semantic information. These two U-shape branches complement each other, keeping the high accuracy of the Encoder-decoder Backbone while maintaining the efficiency and effectiveness of the Two-pathway Backbone. In addition, a Simplified Attention Fusion Module (SAFM) is proposed to effectively fuse different levels of information from two branches for pixel-by-pixel prediction. Experimental results show (on our own constructed dataset) that BUNet achieves the highest Intersection over Union (IoU) and F1-score of 75.96% and 86.34%, respectively, with minimum parameters of 0.93M and 11.94G Floating-point of Operations (FLOPs) in branch segmentation. Meanwhile, BUNet achieves a speed of 110.32 Frames Per Second (FPS) with input image size of 1280 × 720 pixels. These results confirm that the proposed method can effectively detect the branches and can, therefore, be used to plan an obstacle avoidance path for harvesting robots.