A YOLO-Based Method for Head Detection in Complex Scenes

Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to address the challenge of small object detection. For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. These modules are characterized by high detection efficiency and high accuracy. They significantly improve the model's multi-scale detection capabilities, thus enhancing the detection ability of the system. Secondly, it was found in practical operations that the original IoU function has a serious problem with overlapping detection in complex scenes. There is an IoU function that can ensure that the final selection boxes cover the object as accurately as possible without overlapping. This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training.