In this paper, we propose a data-driven proposal and deep-learning based classification scheme for small target detection. Previous studies have shown feasible performance using conventional computer vision techniques such as spatial and temporal filters. However, those are handcrafted approaches and are not optimized due to the nature of the application fields. Recently, deep learning has shown excellent performance for many computer vision problems, which motivates the deep learning-based small target detection. The proposed data-driven proposal and convolutional neural network (DDP-CNN) can generate possible target locations through the data-driven proposal and final targets are recognized through the classification network. According to the experimental results using drone database, the DDP-CNN shows 91% of train accuracy and 0.85 of average precision (AP) of the target detection.
Junhwan Ryu and Sungho Kim, "Small infrared target detection by data-driven proposal and deep learning-based classification," Proc. SPIE 10624, Infrared Technology and Applications XLIV, 106241J (Presented at SPIE Defense + Security: April 19, 2018; Published: 23 May 2018); https://doi.org/10.1117/12.2304677.
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