Crowd-aware Black Pig Detection for Low Illumination

Pig detection is vital to pig farms since it is the basis for counting, weight estimation, and behavior recognition functions. Existing methods focused on white pig detection instead of black pigs because the contrast between white pigs and the background is more evident than black pigs, making black pig detection more challenging. Furthermore, pig farms often suffer from insufficient light, and pigs tend to be stacked together, which makes it hard to detect pigs accurately. To this end, we propose a black pig detection method, based on YOLOV4, robust to both crowd scenes and low-illumination conditions. The method consists of the following parts: 1) generate domain adaptation dataset (DAD) based on style-transfer to optimize the original data distribution, therefore improving the performance of the method for low-illuminated conditions; 2) propose a crowd-aware module (CAM), adapted to YOLOV4 backbone architecture, to generate crowd density maps; 3) develop an adaptive attention module (AAM) to fuse YOLOV4 backbone features with corresponding crowd density map allowing the method robust to pigs in the crowd. The experimental results confirm the feasibility of this method. The mAP value in fully-lighting and poorly-lighting increased to 88.95% and 84.74%, respectively.