Weed Detection in Images of Carrot Fields Based on Improved YOLO v4
Boyu Ying, Yuancheng Xu, Shuai Zhang, Yinggang Shi, Li Liu
Abstract
The accurate weed detection is the premise for precision prevention and control of weeds in fields. Machine vision offers an effective means to detect weeds accurately. For precision detection of various weeds in carrot fields, this paper improves You Only Look Once v4 (YOLO v4) into a lightweight weed detection model called YOLO v4-weeds for the weeds among carrot seedlings. Specifically, the backbone network of the original YOLOv4 was replaced with MobileNetV3-Small. Combined with depth-wise separable convolution and inverted residual structure, a lightweight attention mechanism was introduced to reduce the memory required to process images, making the detection model more efficient. The research results provide a reference for the weed detection, robot weeding, and selective spraying.