Lijun Lou
ABSTRACT
Agricultural pest detection is an important research direction in image detection. Due to the high similarity between many pests and the extremely similar color of pests and background, agricultural pest detection has always been a difficulty. In the early work, many methods were made to improve the detection accuracy by fusing gray space, YUV and HSV. In recent years, with the development of neural networks, the Yolo series began to show better performance in agricultural pest detection. In order to further improve the performance of Yolo series, the method of combining EIOU with Yolov5 is applied in this paper, and the accuracy on IP102 is improved.
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Index Terms
- Agricultural Pest Detection based on Improved Yolov5
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