Journal
SENSORS
Volume 22, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/s22103782
Keywords
small targets detection; combined attention mechanism; multiscale feature fusion; infrared image; multiscale objects
Funding
- National Science and Technology Foundation Strengthening Plan [2021-JCJQ-JJ-1020]
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This paper proposes a CAA-YOLO algorithm to address the challenges in infrared ocean ship detection. By introducing a high-resolution feature layer and a feature fusion method, the performance of small target detection is effectively improved.
Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.
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