EDASNet: efficient dynamic adaptive-scale network for infrared pedestrian detection

计算机科学 增采样 特征(语言学) 卷积(计算机科学) 棱锥(几何) 行人检测 比例(比率) 人工智能 目标检测 特征提取 模式识别(心理学) 计算机视觉 图像(数学) 人工神经网络 行人 数学 物理 量子力学 运输工程 工程类 哲学 语言学 几何学
作者
Yang Liu,Ming Zhang,Fei Fan,Dahua Yu,Jianjun Li
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (11): 115406-115406
标识
DOI:10.1088/1361-6501/ad6bb3
摘要

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 as the backbone for feature extraction. It mainly includes a lightweight adaptive-weight downsampling module and a dynamic enhancement convolution module. In addition, a multi-scale aggregation feature pyramid network 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 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.
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