Pedestrian Detection Algorithm Combining Attention Mechanism and Nonmaximum Suppression Method

Abstract

With the involution of pedestrian detection technology, higher requirements are put forward for the detection accuracy under the conditions of insufficient light, target occlusion, and too small scale. Without information and multiscale pedestrian target, visible light single-mode pedestrian detection algorithm has poor performance. To solve the above problems, a pedestrian detection algorithm combining attention mechanism and nonmaximum suppression method is proposed in this study, aiming to improve the accuracy of pedestrian detection. In addition, residual network ResNet-50 and IoU (intersection over union) loss function are also adopted to improve pedestrian detection accuracy. Attention mechanism was used to optimize and highlight pedestrian area features, and meanwhile, the nonmaximum suppression method was applied to improve the robustness of the algorithm. Experimental results show that the detection accuracy of the proposed algorithm is significantly higher than that of the traditional convolutional neural network algorithm.

Efficient dynamic fusion for multispectral pedestrian detection

“…Pedestrian detection plays a crucial role in various computer vision applications such as autonomous driving, surveillance, and human-computer interaction. [1]- [3] Traditional pedestrian detection algorithms heavily rely on RGB images, which may suffer from limitations under challenging lighting and environmental conditions. In recent years, multispectral pedestrian detection has gained significant attention as it offers the potential to overcome these limitations and improve detection performance.…”