CNN-based infrared dim small target detection algorithm using target-oriented shallow-deep features and effective small anchor

For the extremely small size and low signal-to-clutter ratio, target detection in infrared images is still a considerable challenge. Specifically, it is very difficult to detect the point targets because there is no texture and shape information can be used. A target-oriented shallow-deep feature-based detection algorithm is proposed, opening up a promising direction for convolutional neural network-based infrared dim small target detection algorithms. To ensure that small target instances can be used correctly for networks, the effective small anchor is designed according to the shallow layer of ResNet50. To determine whether a detection result belongs to the target, the authors depend on whether the detection centre is included in the ground truth area, rather than on the Intersection Over Union overlap rate, which avoids misjudging the detection result. In this way, small targets can be trained and detected correctly through ResNet50. More importantly, the authors demonstrate that spatially finer shallow features are crucial for small target detection and that semantically stronger deep features are helpful for improving detection probability. Experimental results on simulation data sets and real data sets show that the proposed algorithm can detect the point target when the local signal-to-clutter ratio is approximately 1.3, displaying infinite advantage and great potentiality.