Accurate detection of weeds is a key technology for developing automated weeding equipment. To address the problems of high detection complexity and poor robustness resulting from the complex distribution and variety of weeds, we proposed a weed detection approach for vegetable seedling based on the improved YOLOv5 algorithm and image processing, implemented on a self-developed mobile robot platform. The weed detection complexity was reduced by indirectly detecting weeds through identifying vegetables, thus improving the detection accuracy and robustness. The convolutional block attention module (CBAM) attention module was added to the backbone feature extraction network of the YOLOv5 object detection algorithm to enhance the focus of the network on vegetable targets, and the Transformer module was added to enhance the global information capture capability. The results showed that the average detection accuracy of the improved YOLOv5 algorithm for vegetable targets could reach 95.7%, which was increased by 5.8%, 6.9%, 10.3%, 13.1%, 9.0%, 5.2%, and 3.2% compared with Faster R-CNN, SSD, EfficientDet, RetinaNet, YOLOv3, YOLOv4, and YOLOv5, respectively. The average detection time of the algorithm for a single run was 11 ms, indicating good real-time performance. The method defined green plants outside the vegetable border as weeds, and combined the extreme green (ExG) with the OTSU threshold segmentation method to segment weeds from the soil background. Finally, the weed connectivity domain was marked, followed by outputting the weed plasmids and detection frames. The proposed method could provide a technical reference for automated precision weeding in agriculture.
