An Improved Multi Target Ship Recognition Model Based on Deep Convolutional Neural Network

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 28; no. 1; pp. 216 - 223
• Main Authors: Li, Shu-Hua, Yan, Feng-Long, Li, Ying-Qiu
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 20.01.2024
• Subjects: Algorithms; Artificial neural networks; Cluster analysis; Clustering; Frames per second; Machine learning; Neural networks; Object recognition; Radar imaging; Ships; Synthetic aperture radar; Target recognition; Vector quantization; Vessels
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2024.p0216

Deep learning is the major technique used to identify objects in images captured by the synthetic aperture radar (SAR). While SAR images can be used to identify ships in general, detecting multiple ships or small vessels in these images in complex contexts remains an outstanding challenge. This study proposes a model of detection based on the improved PP-YOLO deep convolutional neural network that can identify multiple ships as well as small vessels in complex scenarios from SAR images. The histogram equalization algorithm is first used to preprocess the SAR images, and then the initial anchor box is optimized by using the shape similarity distance-based K -means clustering algorithm. Following this, the accuracy of the training network is improved based on the feature pyramid network and an attention mechanism. The experimental results show that the average accuracy (average precision) of the model was 94.25% at 41.63 frames per second on the GF-3 and the Sentinel-1 SAR datasets, superior to those of YOLOv3 (Darknet), YOLOv7, FPN (VGG), SSD, Faster R-CNN, and PP-YOLO (RestNet50-vd). The model also satisfies the demands of real-time detection.