ResFuseYOLOv4_Tiny: Enhancing detection accuracy for lightweight networks in infrared small object detection tasks

Abstract

When deploying infrared target detection systems at the edge, one often encounters constraints on computational and storage resources. In such scenarios, lightweight networks prove to be a viable detection solution. Compared to large-scale deep networks, lightweight networks, after discarding redundant structures, often do not achieve satisfactory detection accuracy, especially in the context of small object detection tasks. This paper proposes ResFuseYOLOv4_Tiny, an improved lightweight network specifically designed for the effective detection of infrared small objects, particularly in resource-constrained scenarios. We extend the YOLOv4_Tiny neck network to enable efficient feature extraction. By integrating Res_head with residual structures and fusion-based feature reuse, our framework substantially enhances the capability to detect small objects by expanding the receptive field and effectively merging features from various receptive fields. Additionally, we introduce a drop and adaptive parameter-based attention mechanism called DCA, leveraging the inclusion of drop layers to effectively reduce information redundancy. Moreover, we replace the conventional CIoU loss function with our customized MSIoU, incorporating parameter m to adaptively adjust the loss weights in response to the influence of small target sizes. Experimental results conducted on synthetic and real infrared small object datasets respectively reveal a significant 14.47% and 11.28% improvement in detection accuracy. The proposed ResFuseYOLOv4_Tiny framework presents a viable solution for lightweight networks when both detection speed and accuracy are concurrently considered.