Nighttime cattle detection based on YOLOv4

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