Automatic detection and counting of small yellow thrips on lotus leaf back based on YOLO combined with VDSR and DPSR network

Cheng-Hsuan Lin; Pin-Hsiu Chen; ChinSu Lin; Ying-Cheng Chen; Mei-Jing Huang; Wei-Min Liu

doi:10.1117/12.2601705

30 June 2021 Automatic detection and counting of small yellow thrips on lotus leaf back based on YOLO combined with VDSR and DPSR network

Cheng-Hsuan Lin, Pin-Hsiu Chen, ChinSu Lin, Ying-Cheng Chen, Mei-Jing Huang, Wei-Min Liu

Author Affiliations +

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780L (2021) https://doi.org/10.1117/12.2601705
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Pest detection is a critical issue for farmers and pest analysts and requires immediate response to control the damage. However, one of the current pest control evaluation strategies is to manually mark the pests from the sample image and count them, which is tedious and time-consuming. This paper presents a method to automatically detect and count the number of small yellow thrips (SYT) on lotus leaf back based on YOLO. Due to the natural curved structure of the leaf, some parts of its picture will be out of focus and therefore result in inaccurate counting results. In order to reduce the impact of the out-of-focus area, we first proposed a novel SYT-simulation experiment and then trained the Very-Deep Super-Resolution (VDSR) and Deep plug-and-play super-resolution (DPSR) neural network to enhance the blurred area. With such image enhancement, the final detection rate can be improved from 70.41% to 80.17% after passing a YoLobased object detection network.

Citation Download Citation

Cheng-Hsuan Lin, Pin-Hsiu Chen, ChinSu Lin, Ying-Cheng Chen, Mei-Jing Huang, and Wei-Min Liu "Automatic detection and counting of small yellow thrips on lotus leaf back based on YOLO combined with VDSR and DPSR network", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780L (30 June 2021); https://doi.org/10.1117/12.2601705

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