Nighttime cattle detection based on YOLOv4

Feng Wu; Hongke Zhao; Meili Wang

doi:10.1117/12.2589473

27 January 2021 Nighttime cattle detection based on YOLOv4

Feng Wu, Hongke Zhao, Meili Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172008 (2021) https://doi.org/10.1117/12.2589473
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Infrared image from nighttime cattle farm has low contrast, blurred visual effect and unclear details. We proposed a method based on dark channel prior as well as piecewise linear stretch to enhance infrared image. Improvement of image quality contributes to manually annotating images more accurately when preparing the dataset. The results of image enhancement are compared with other methods to evaluate the performance. Further more, we verify its performance on nighttime cattle detection based on YOLOv4. We get appropriate prior anchor boxes for this work by K-means clustering on cattle image dataset. YOLOv4 models of cattle detection are trained with datasets of original images and processed images. A total of 1400 cattle images from different scenes have been collected from surveillance videos as a dataset for experiment. The average precision (AP) of cattle detection is more than 95%. Compared to control groups, the APs from enhanced images are 0.64% and 0.70% higher. Experimental results show that image enhancement can improve the accuracy of nighttime cattle detection based on YOLOv4

Citation Download Citation

Feng Wu, Hongke Zhao, and Meili Wang "Nighttime cattle detection based on YOLOv4", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172008 (27 January 2021); https://doi.org/10.1117/12.2589473

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