Robust tomato target detection method based on YOLOv8
Aiming at the problems of low recognition precision,many false detection and missed detection of tomato target detection algorithms based on the YOLO series in the current complex environment,a single-stage tomato target detection and recognition method based on YOLOv8 was proposed.This method takes the single-stage target detection algorithm of YOLOv8 as the main body,combined with the improvement of the CSPDark-net structure,the introduction of attention mechanism,the use of adaptive anchor boxes and mixed loss functions,etc,to improve the detection precision and efficiency of the algorithm.Experimental results show that the YOLOv8 model can still efficiently and accurately identify tomato targets in complex environment with good ro-bustness,which provides reliable technical support for target detection in agricultural automation.
tomatoYOLOv8CSPDarknet structureattention mechanismmixed loss function
万方数据
维普
