Citrus detection application of based on improved YOLOv3 algorithm

Xiaokan Wang; Jinhui Long

doi:10.1117/12.2675534

25 May 2023 Citrus detection application of based on improved YOLOv3 algorithm

Xiaokan Wang, Jinhui Long

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263614 (2023) https://doi.org/10.1117/12.2675534
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In order to solve the problem of missed detection and false detection caused by light change, leaf occlusion, fruit overlap and other citrus detection factor, an improved YOLOv3 citrus detection depth learning algorithm is proposed to achieve accurate and rapid recognition of citrus in natural environment. The algorithm uses image scaling and rotation transformation, homomorphic filtering processing and other methods to process the citrus object pictures. The 1 − IOU value is the distance function, and the genetic algorithm based on K-means clustering is used to optimize the initial anchor box size, all that are enriched the citrus image data set. So the algorithm achieves effective image enhancement and improves the detection resolution and accuracy. The experimental results show that the average accuracy of the citrus detection algorithm reaches 88.7%, the average detection time of single width pixels 640×448 pixels is 131 ms and the system efficiency increases by 9.6%.

Citation Download Citation

Xiaokan Wang and Jinhui Long "Citrus detection application of based on improved YOLOv3 algorithm", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263614 (25 May 2023); https://doi.org/10.1117/12.2675534

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