Infrared Dim and Small Target Detection Based on Dual-Channel Feature-Enhancement Integrated Attention Network
CAI Yunze 1，2，3，4，5， ZHANG Yanjun 1，2，3，4，5
1. Department of Automation， Shanghai Jiao Tong University， Shanghai， 200240；
2. Key Laboratory of System Control and Information Processing， Ministry of Education of China， Shanghai， 200240；
3. Shanghai Engineering Research Center of Intelligent Control and Management， Shanghai 200240；
4. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education， Shanghai Jiao Tong University， Shanghai， 200240；
5. Institute of Marine Equipment， Shanghai Jiao Tong University， Shanghai， 200240
Abstract Aiming at the problems existing in long-distance infrared target detection, such as less feature information, complex environment, more noise interference, and high missed detection rate and false alarm rate of traditional target detection algorithms, an infrared small target detection algorithm based on dual-channel feature enhancement attention network is proposed in this paper. The overall network structure mainly includes three parts: dual channel feature extraction module, feature enhancement module and integrated top-bottom attention module. Compared with single channel feature extraction, dual channel feature extraction can obtain more feature information. Feature enhancement module can enrich target features further. Moreover, the integrated top bottom attention module can adaptively enhance target features and weaken background noise. And then the algorithm improves the detection effect of dim and small targets in the infrared images. Finally, it is verified that the algorithm proposed in this paper has a better detection effect, and has a lower rate of missed detection and false detection.
2021, Vol. 4 
