A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception

Abstract

Visual perception has become a prerequisite for automated jujube harvesting robot operations under complex orchard conditions. Catch-and-Shake harvesting, as the most efficient and common harvesting method, has widely been applied on various manually operated harvesters to complete large-area jujube fruit harvesting. However, the main factors restricting the development of existing harvesters are labor shortage, high labor cost, and low operating efficiency. To address the issues, we designed a catch-and-shake harvesting robot for jujube tree trunks and branches visual perception that can provide a barrier-free catch-and-shake operation area and guide the manipulator to reach the area to complete the harvesting operation. Meanwhile, a visual perception system including tree trunks and branches detection, skeleton extraction, catch-and-shake area confirmation was presented to guide robot intelligent operations. In the visual perception system, a novel salientobjectdetectionmodel called feature intersection and fusion Transformer (FIT-Transformer) network was proposed to split branches and background to provide reference for determining safe catch-and-shake areas. Moreover, we designed a diverse feature aggregation (DFA) and an attention feature fusion module (AFFM) to strengthen feature learning capabilities and obtain robust perception models. Comparative experimental results showed that our proposed FIT-Transformer model outperformed 12 state-of-the-art (SOTA) algorithms including C2FNet, RAS, BASNet, U2Net, SCRNet, PiCANet, EDRNet, EGNet, ICONR, VST, TransSOD and ABiU_Net. Specifically, the segmentation accuracy of jujube tree trunks and branches using our method showed the satisfactory result on five evaluation indexes under natural environment (the EM, SM, WF, FM and MAE reached 0.9713, 0.8991, 0.8854, 0.8905, and 0.0302, respectively). Field experiments also proved that our method could meet the requirements of operational accuracy and real-time operations.