EDASNet: Efficient Dynamic Adaptive-scale Network for Infrared Pedestrian Detection

Abstract

Abstract Infrared images are widely utilized due to their exceptional anti-interference capabilities. However, challenges such as low resolution and an absence of detailed texture can impede the effective recognition of multi-scale target information, particularly for small targets. To address these issues, we introduce a multi-scale detection framework named Efficient Dynamic Adaptive-scale Network (EDASNet), which focuses on enhancing the feature extraction of small objects while ensuring efficient detection of multi-scale. Firstly, we design a Lightweight dynamic enhance network (LDENet) as the backbone for feature extraction. It mainly includes a Lightweight adaptive-weight down sampling module and a dynamic enhancement convolution module. In addition, a multi-scale aggregation feature pyramid network (MSA-FPN) is proposed, which improves the perception effect of small objects through a multi-scale convolution module. Then, the Repulsion Loss term was introduced based on CIOU to effectively solve the missed detection problem caused by target overlap. Finally, the dynamic head (DyHead) was used as the network detection head, and through the superposition of dynamic convolution and multiple attention, the network was able to accurately realize multi-scale object detection. Comprehensive experiments show that EDASNet outperforms existing efficient models and achieves a good trade-off between speed and accuracy.