Abstract
Focusing on the problems of CenterNet in infrared images, such as feature loss and insufficient information utilization, an improved algorithm based on spatial feature enhancement is proposed. Firstly, a frequency-space enhancement module is used to enhance the details of the target region. Secondly, a module that can count global information is introduced into the backbone network to model the feature graph globally. Finally, in the case of no increase in computation and complexity, the residual mechanism is adopted to redesign the overall structure of the algorithm, which strengthens the feature interaction simply and efficiently. Experimental tests are carried out on the self-established infrared object detection dataset G-TIR and public infrared object detection dataset FLIR. The proposed algorithm improves the accuracy of the baseline by 8.4% and 15.3% respectively, and is better than many mainstream object detection algorithms in recent years. Meanwhile, the detection speed reaches 72 FPS, which balances the detection accuracy and speed well, then meet the real-time detection requirements.
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Guo, H., Hou, Z., Sun, Y., Li, J., Ma, S. (2022). Infrared Object Detection Algorithm Based on Spatial Feature Enhancement. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_28
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DOI: https://doi.org/10.1007/978-3-031-18916-6_28
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