Detection of the farmland plow areas using RGB-D images with an improved YOLOv5 model

Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment. To precisely recognize these boundaries, a detection method for unmanned tractor plow areas based on RGB-Depth (RGB-D) cameras was proposed, and the feasibility of the detection method was analyzed. This method applied advanced computer vision technology to the field of agricultural automation. Adopting and improving the YOLOv5-seg object segmentation algorithm, first, the Convolutional Block Attention Module (CBAM) was integrated into Concentrated-Comprehensive Convolution Block (C3) to form C3CBAM, thereby enhancing the ability of the network to extract features from plow areas. The GhostConv module was also utilized to reduce parameter and computational complexity. Second, using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model, the mask image was processed to extract contour boundaries, align the contours with the depth map, and obtain the boundary distance information of the plowed area. Last, based on farmland information, the calculated average boundary distance was corrected, further improving the accuracy of the distance measurements. The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99% for plowed areas and that the ranging accuracy improved with decreasing detection distance. The ranging error at 5.5 m was approximately 0.056 m, and the average detection time per frame is 29 ms, which can meet the real-time operational requirements. The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.