Deep learning-based object detection approaches have shown excellent performance in RGB images. However, when used to detect objects from infrared images, the accuracy may reduce significantly due to low contrast, obscure textures and strong noise of infrared images. To alleviate the problem, we design a detail enhancement module involving spatial attention mechanism to enhance the textures and details of images. The output of the proposed module is fed into modified YOLOv4. We introduce Alpha-IoU loss and Weighted-NMS to YOLOv4 to enhance geometric factors in both bounding box regression and Non-Maximum Suppression, leading to notable gains of average precision. The experiment results show that compared with YOLOv4, mAP0.5 and mAP0.5:0.95 of our model are improved by 1.1% and 3.5% respectively, effectively improving the detection accuracy.
Siamese trackers have attracted great attention on visual object tracking due to their real-time speed and high accuracy. In this paper, we propose a dual path aggregation network (SiamDPAN) for high-performance tracking. First, we build a multi-level similarity maps aggregation (MSA) structure, which predicts and fuses the similarity maps from multi-level features. Second, we propose a mask path aggregation module (MPA) for better capturing the appearance changes of objects by propagating maps in low-layers. We conduct sufficient ablation studies to demonstrate the effectiveness of our proposed tracker. We only train our network with two datasets, achieving 0.436 EAO and 0.351 EAO on VOT2016 and VOT2018.
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