Shape Similarity Intersection over Union Loss Hybrid Model for Detection of Synthetic Aperture Radar Small Ship Objects in Complex Scenes

With the continuous development and utilization of marine environments, the demand for accurate identification of ship targets at sea is increasing in both military and civilian fields. Synthetic aperture radar (SAR) is used to detect ship targets at sea and can provide 24-h detection under any weather conditions. Deep-learning models enable the effective detection of ship targets using SAR images; however, the recognition accuracy may be low or false positives may occur in complex scenarios wherein it is difficult to detect the ship targets. Current target-detection tasks include target classification and positioning through bounding-box regression. Herein, a regression loss function is derived to calculate the position of the bounding box, and intersection over union (IoU) is applied to estimate the positioning accuracy. As a result, a gap exists between the commonly used positioning losses for regressing the parameters of a bounding box and the optimization of these metric values. Therefore, the proposed hybrid model combines classification, localization, and segmentation with a novel multi-task loss function for boundary-box localization based on the improved IoU. This solves the problem of inconsistency between training and evaluation and improves the positioning accuracy. Experiments were conducted using the SAR dataset for ship detection; the dataset was labeled by SAR experts and included multi-scale ship chips in both range and azimuth. In summary, the experimental results indicate that the proposed hybrid model could improve the detection accuracy in complex scenarios, and its false positive rate is significantly lower than those of the other models.