Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM

Improper regulation of sugarcane feed volume can lead to harvester inefficiency or clogging. Accurate recognition of feed volume is therefore critical. However, visual recognition is challenging due to sugarcane stacking during feeding. To address this, we propose YOLO-ASM (YOLO Accurate Stereo Matching), a novel detection method. At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. This significantly reduces missed detections and low-confidence predictions in dense stacking scenarios, improving detection speed by 28.04% and increasing mean average precision (mAP) by 5.31%. At the stereo matching level, we enhance the SGBM (Semi-Global Block Matching) algorithm through improved cost calculation and cost aggregation, resulting in Opti-SGBM (Optimized SGBM). This double-cost fusion approach strengthens texture feature extraction in stacked sugarcane, effectively reducing noise in the generated depth maps. The optimized algorithm yields depth maps with smaller errors relative to the original images, significantly improving depth accuracy. Experimental results demonstrate that the fused YOLO-ASM algorithm reduces sugarcane volume error rates across feed volumes of one to six by 3.45%, 3.23%, 6.48%, 5.86%, 9.32%, and 11.09%, respectively, compared to the original stereo matching algorithm. It also accelerates feed volume detection by approximately 100%, providing a high-precision solution for anti-clogging control in sugarcane harvester conveyor systems.