Pedestrian detection based on SENet with attention mechanism

Pedestrian detection is a fundamental task in computer vision with wide-ranging applications such as autonomous driving, surveillance, and human-computer interaction. The advancement of deep learning has revolutionized pedestrian detection, enabling more accurate and robust detection systems. In this study, we propose a novel approach that combines the power of SENet with the attention mechanism to improve pedestrian detection performance. We proposed the pedestrian detection technology based on the SENet attention mechanism consists of several key steps. First of all, the use of residual neural network backbone advanced features extracted from the input image; Then introduces SENet based architecture to the attention of the module, characteristic response to realign the channel direction, emphasizes the information related to the pedestrian characteristics, at the same time suppressing irrelevant details, our attention to the existing channel module modified, increase the space information learning in the feature maps, highlight the characteristics related to the target area in the figure, is helpful to keep out the pedestrian space characteristic of effective information.