NSD‐SSD: A Novel Real‐Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

Abstract

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

“…Based on the experimental results presented in Table 10, the algorithms proposed in this study exhibited superior performance compared to the widely used algorithms in ship target detection. Notably, the Faster R-CNN and SSD algorithms are limited in their adaptability to multi-scale ship targets due to fixed anchor frame parameters, resulting in relatively low detection accuracy [32,33]. Although the YOLOv5 and YOLOv7 algorithms demonstrated faster detection speeds (47 and 54 FPS, respectively), their detection accuracies remained relatively low at 89.6% and 90.8%, respectively.…”

Multi-Sensor-Based Hierarchical Detection and Tracking Method for Inland Waterway Ship Chimneys

“…Wang Y combined single-shot multibox detector (SSD) with migration learning to solve the ship detection problem in complex environments, such as oceans and islands [20]. Sun J, based on the SSD model, integrated expansion convolution with a multiscale feature fusion to improve small target detection accuracy [21]. Not coincidentally, Chen P, to improve the small target detection accuracy, embedded the elemental pyramid model into the traditional RPN, and then mapped it to a new elemental space for object recognition [22].…”

A lightweight ship target detection model based on improved YOLOv5s algorithm

“…To better fuse, the information between different layers, the output of the four layers is adjusted to the same resolution and the same number of channels for weighted fusion to obtain the optimal fusion weight, which further improves the recognition ability of small targets, but also improved the model complexity and calculation amount correspondingly. Sun [ 18 ] proposed an SSD-based ship detection model, NSD-SSD. Firstly, expansion convolution was combined with multi-scale feature fusion to improve the detection performance of small targets.…”