UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images

Abstract

Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy.

Small object intelligent detection method based on adaptive recursive feature pyramid

“…To solve these problems, the researchers optimize the small object detection method based on various optimization strategies, such as data enhancement [14] , [15] , [16] , [17] , [18] , multi-scale learning [19] , [20] , [21] , [22] , context learning [23] , [24] , [25] , [26] , [27] , and generative confrontation learning [28] , [29] , [30] , [31] , [32] , [33] , [34] , which are analyzed as follows:…”

A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head

“…Due to its all-day, all-weather features, Synthetic Aperture Radar (SAR), which can serve as a persistent surveillance and reconnaissance platform [1,2], has been widely used in civilian and military fields [3,4], such as geological and ecological surveillance, automatic driving, target detection, and so forth [5][6][7]. Effective detection of targets in SAR images under different conditions is of great significance in the practical applications of SAR images, especially in civil surveillance and military reconnaissance.…”