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2 March 2022 Agricultural pest detection algorithm based on improved faster RCNN
Zhenghao Wang, Lifeng Qiao, Mengke Wang
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Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580H (2022) https://doi.org/10.1117/12.2626859
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China
Abstract
The agricultural losses caused by the difficulty of pest detection and unscientific detection are increasing year by year. The traditional detection and identification methods of agricultural pests have low accuracy and cannot meet the pest control needs of agricultural planters. The accuracy of agricultural pest detection is the most important thing in solving the problem. Therefore, this paper proposes an agricultural pest detection algorithm based on improved Faster RCNN. First, we use the improved FPN combined with the backbone network to expand the low-level receptive field and enhance the algorithm's feature extraction ability for small targets. Then use the bilinear interpolation method of the ROI Align algorithm to replace the rounding quantization in the ROI Pooling algorithm for calculation, thereby improving the detection accuracy of small targets. Finally, we add a Convolutional Block Attention Module (CBAM) to the backbone network to enhance the effectiveness of feature extraction. Experiments on the detection algorithm on the natural scene data set we have compiled, the mean average accuracy (mAP) reaches 87.7%, which is a large improvement compared with the original algorithm.
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Zhenghao Wang, Lifeng Qiao, and Mengke Wang "Agricultural pest detection algorithm based on improved faster RCNN", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580H (2 March 2022); https://doi.org/10.1117/12.2626859
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KEYWORDS
Detection and tracking algorithms
Agriculture
Target detection
Feature extraction
Quantization
Image enhancement
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