| Summary: | Despite the achievements of deep neural-network-based object detection, detecting small objects in low-resolution images remains a challenging task due to limited information. A possible solution to alleviate the issue involves integrating super-resolution (SR) techniques into object detectors, particularly enhancing feature maps for small-sized objects. This paper explores the impact of high-resolution super-resolved feature maps generated by SR techniques, especially for a one-stage detector that demonstrates a good compromise between detection accuracy and computational efficiency. Firstly, this paper suggests the integration of an SR module named feature texture transfer (FTT) into the one-stage detector, YOLOv4. Feature maps from the backbone and the neck of vanilla YOLOv4 are combined to build a super-resolved feature map for small-sized object detection. Secondly, it proposes a novel SR module with more impressive performance and slightly lower computation demand than the FTT. The proposed SR module utilizes three input feature maps with different resolutions to generate a super-resolved feature map for small-sized object detection. Lastly, it introduces a simplified version of an SR module that maintains similar performance while using only half the computation of the FTT. This attentively simplified module can be effectively used for real-time embedded systems. Experimental results demonstrate that the proposed approach substantially enhances the detection performance of small-sized objects on two benchmark datasets, including a self-built surveillance dataset and the VisDrone2019 dataset. In addition, this paper employs the proposed approach on an embedded system with a Qualcomm QCS610 and demonstrates its feasibility for real-time operation on edge devices.
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