A Low-Complexity Human Head Detection Network Based on YOLOv6
Zhenyang Hu, Jiahao Yu, Gaofei Sun, Zhenjiang Qian, Xiaobing Xian, Miaomiao Zhu · 2023
Indoor and outdoor surveillance have become essential aspects of public safety. In recent years, intelligent surveillance has made a lot of progress, with human head detection becoming a common technique in this field. However, there are some challenges in human head detection such as failure to detect targets, lighting conditions, and crowded areas. In this paper, we proposed an improved head network that decreases the number of YOLOv6-n model parameters while simultaneously increasing detection accuracy. Additionally, to accelerate model convergence, we designed a box regression loss function, QIoU, which converges in terms of the bounding box's width and height, as well as its X and Y axes center positions. Our improved algorithm achieves 91.6% AP and 52.2% AR on SCUT-HEAD dataset, respectively, which are higher than the baseline with SIoU loss function. Finally, our method also has better accuracy performance on small targets (i.e., 47.6 %).