Tomato Fruit Recognition Based on YOLOX-L-TN Model
Aiming at the intelligent demand for tomato picking operation in plant factories,in order to overcome the problems of low recognition accuracy and low speed caused by different sizes and overlapping of tomato fruits during picking operation,an improved target recognition model of YOLOX-L-TN was proposed,in which a TN module containing residual structure was designed according to the internal structure and principle of channel and spatial attention mechanism of feature graph,and integrated into the backbone network of YOLOX-L.This model improved the speed and accuracy of model recognition while maintaining the lightweight of the network.Compared with YOLOX-L,the AP value of YOLOX-L-TN was increased by 4.81 percentage points,and the recognition time of single image is increased by 0.141 7 s,and the optimal position of TN module was between the input and the backbone network.Furthermore,TN module was compared with similar modules SENet,CAM,CBAM and CAM,and the results showed that AP value was increased by 0.53,4.19,6.12 and 6.34 percentage points,respectively,and the recognition time of single image is increased by 0.019 1,0.025 0,0.021 1,0.018 9 s,respectively.In conclusion,the proposed YOLOX-L-TN model had the advantages of high precision,fast identification speed and high robustness,which provided technical support for the intelligent picking of tomatoes in the later stage.
国家科技期刊平台
NSTL
万方数据
维普
