Abstract
Due to the rapid development of chip technology and deep learning revolution, many ship detection frameworks for synthetic aperture radar (SAR) imagery based on convolutional neural networks (CNNs) have been proposed and achieved great success. However, there are problems hampering their development: 1) For the SAR ship detection task, it is uneconomic to apply heavy backbone network to extract features because it results in heavy computing load and prolongs the inference time cost; 2) The anchor-based methods usually have massive hyper-parameters, which typically need to be tuned carefully and easily lead to weak detection performance. To alleviate the problems, an efficient low-cost ship detection network for SAR imagery is proposed in this paper. Firstly, a simplified U-Net as the backbone to extract features is proposed. It only contains ~0.47 million learnable weights, which is 2.37%, 0.76%, 0.34%, 1.01%, 0.55% and 1.07% of DarkNet-19, DarkNet-53, VGG-16, ResNet-50, ResNet-101 and ResNext-101, respectively. Secondly, an anchor-free SAR ship detection framework consisting of a bounding boxes regression sub-net and a score map regression sub-net based on simplified U-Net is proposed. To evaluate the effectiveness of our method, extensive experiments have been conducted and a more comprehensive set of evaluation metrics have been applied. Results demonstrate that the proposed network achieves 68.1% average precision and 67.6% average recall on the SAR ship detection dataset (SSDD), respectively. Compared with the state-of-the-art works, our proposed network achieves very competitive detection performance and extreme lightweight (~0.93 million learnable weights in total).
Highlights
Marine traffic is increasingly crucial for global, regional and national economies and security because it significantly benefits seaborne trade and defends illegal activities, including smuggling, territorial sea invasion and maritime terrorism
Numerous research achievements have been published in the field of synthetic aperture radar (SAR) ship detection
In [5], Ai et al proposed a 2-D joint lognormal distribution algorithm utilizing a strong gray intensity correlation to model the clutter of ship targets
Summary
Marine traffic is increasingly crucial for global, regional and national economies and security because it significantly benefits seaborne trade and defends illegal activities, including smuggling, territorial sea invasion and maritime terrorism. To ensure effective and efficient marine traffic control, intelligent ocean surface ship surveillance, which is based on remote sensing imagery and computer-vision technique, has become a hot research field in recent years. High-resolution synthetic aperture radar (SAR) is regarded as one of the most suitable sensors for instances detection and maritime monitoring in the field of space technology, for it offers highresolution images regardless of weather and light conditions. Numerous research achievements have been published in the field of SAR ship detection. The most widely used method is Constant False-Alarm Rate (CFAR) algorithm. This algorithm sets a threshold so that we can identify targets that are statistically significant above the background pixel while maintaining a constant false alarm rate [1]–[13].
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