Abstract
Ship detection is one of the most important research contents of ship intelligent navigation and monitoring. As a supplement to classical navigational equipment such as radar and the Automatic Identification System (AIS), target detection based on computer vision and deep learning has become a new important method. A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection. However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications. On the basis of YOLOv3, this paper proposes a lightweight ship detection model (LSDM) in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace normal convolution. The two improvements reduce parameters and optimize the network structure greatly. The experimental results show that, with only one-third of parameters of YOLOv3, the LSDM has higher accuracy and speed for ship detection. In addition, the LSDM is simplified further by reducing the number of densely connected units to form a model called LSDM-tiny. The experimental results show that, LSDM-tiny has similar detection speed with YOLOv3-tiny, but has a lot higher accuracy.
Highlights
Object detection technologies based on deep learning have received more and more attention in the areas of ship intelligent navigation and ship monitoring [1, 2]. e rapid perception of the navigational environment is the prerequisite for ships to sail safely or for shore base to monitor ships real-time [3]. e perception data collected by radar and the Automatic Identification System (AIS) play an important role in these application areas [4]
All detection algorithms are generally trained on large public data sets to pursue high accuracy; they require high hardware performance to be trained and executed
As the YOLOv3 has a relatively balanced performance in detection accuracy and speed [15] and the DenseNet has obvious effect on reducing parameters, this paper focused on researching lightweight ship detection models by combining the YOLOv3 and DenseNet and provided a new lightweight detector for high-accuracy ship detection. e contribution of this paper includes the following: (1) We propose a lightweight ship detection model called the LSDM, with one-third parameters of the YOLOv3 network, and higher average accuracy of 94% for ship detection
Summary
Object detection technologies based on deep learning have received more and more attention in the areas of ship intelligent navigation and ship monitoring [1, 2]. e rapid perception of the navigational environment is the prerequisite for ships to sail safely or for shore base to monitor ships real-time [3]. e perception data collected by radar and the AIS play an important role in these application areas [4]. As the YOLOv3 has a relatively balanced performance in detection accuracy and speed [15] and the DenseNet has obvious effect on reducing parameters, this paper focused on researching lightweight ship detection models by combining the YOLOv3 and DenseNet and provided a new lightweight detector for high-accuracy ship detection. (1) We propose a lightweight ship detection model called the LSDM, with one-third parameters of the YOLOv3 network, and higher average accuracy of 94% for ship detection (2) We propose a simpler version of the LSDM called LSDM-tiny, with one-eighth parameters of the YOLOv3 network, double detection speed, and average accuracy of 93.5% for ship detection e rest of the paper is organized as follows.
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