JACIII Vol.28 No.1 pp. 216-223
doi: 10.20965/jaciii.2024.p0216

Research Paper:

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

Shu-Hua Li*,†, Feng-Long Yan**, and Ying-Qiu Li**

*Department of Computer Science and Technology, Guangdong University of Science and Technology
Dongguan, Guangdong 523083, China

Corresponding author

**School of Computer and Software, Dalian Neusoft University of Information
No.8 Software Park Road, Dalian, Liaoning 116023, China

May 29, 2023
October 2, 2023
January 20, 2024
feature pyramid network, synthetic aperture radar images, ship recognition, optimization of anchor boxes

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.

Gray intensity values histogram before and after equalization

Gray intensity values histogram before and after equalization

Cite this article as:
S. Li, F. Yan, and Y. Li, “An Improved Multi Target Ship Recognition Model Based on Deep Convolutional Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 216-223, 2024.
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Last updated on Feb. 19, 2024