Ship Target Detection Algorithm Based on Improved Faster R-CNN

Qi, Liang; Li, Bangyu; Chen, Liankai; Wang, Wei; Dong, Liang; Jia, Xiuzhi; Huang, Jing; Ge, Chengwei; Xue, Ganmin; Wang, Dong

doi:10.3390/electronics8090959

Cited by 65 publications

(42 citation statements)

References 21 publications

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“…An et al [20] suggested an improved RBox-based target detection framework to obtain accuracy recall rate and precision of the detection. Qi et al [21] Improved Faster-RCNN by completing image downscaling to obtain useful information of ship images, which helps in the accurate and timely detection of ship images. Zhang et al [22] Proposed a lightweight optimization network LFO net based on SSD for ship detection in SAR images.…”

Section: It Is Fastmentioning

confidence: 99%

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Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

2021

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Ship detection and tracking is an important task in video surveillance in inland waterways. However, ships in inland navigation are faced with accidents such as collisions. For collision avoidance, we should strengthen the monitoring of navigation and the robustness of the entire system. Hence, this paper presents ship detection and tracking of ships using the improved You Only Look Once version 3 (YOLOv3) detection algorithm and Deep Simple Online and Real-time Tracking (Deep SORT) tracking algorithm. Three improvements are made to the YOLOv3 target detection algorithm. Firstly, the Kmeans clustering algorithm is used to optimize the initial value of the anchor frame to make it more suitable for ship application scenarios. Secondly, the output classifier is modified to a single Softmax classifier to suit our ship dataset which has three ship categories and mutual exclusion. Finally, Soft Non-Maximum Suppression (Soft-NMS) is introduced to solve the deficiencies of the Non-Maximum Suppression (NMS) algorithm when screening candidate frames. Results showed the mean Average Precision (mAP) and Frame Per Second (FPS) of the improved algorithm are increased by about 5% and 2, respectively, compared with the existing YOLOv3 detecting Algorithm. Then the improved YOLOv3 is applied in Deep Sort and the performance result of Deep Sort showed that, it has greater performance in complex scenes, and is robust to interference such as occlusion and camera movement, compared to state of art algorithms such as KCF, MIL, MOSSE, TLD, and Median Flow. With this improvement, it will help in the safety of inland navigation and protection from collisions and accidents.

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Section: It Is Fastmentioning

confidence: 99%

“…Inland waterways have become major means of transportation in many parts of the world as reported by different researchers [8,17,21]. However, transportation in these waterways is faced with different challenges-such as collisions-which can result in accidents and death of people.…”

Section: Motivationmentioning

confidence: 99%

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

2021

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“…In view of the problems that the decimal point of digital instrument in the actual scene of substation is prone to false detection and leak detection, and the accuracy of number identification is not high. The paper innovatively combines Faster R-CNN [13][14][15] and YOLOv4 [16] target detection algorithms with connected domain analysis methods. A target detector was formed, and experiments were carried out on the image data actually collected by the substation and compared with the detector composed of Faster R-CNN and YOLOv4.…”

Section: Related Workmentioning

confidence: 99%

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

et al. 2021

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Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

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“…To implement real-time ship detection, Qi et al proposed an improved Faster R-CNN algorithm by scene reduction technology to reduce the target scale during searching [32]. Lin et al proposed a new network for ship detection in SAR Images based on the Faster R-CNN, which improved the detection performance and execution speed through squeeze and excitation mechanisms [33].…”

Section: Related Workmentioning

confidence: 99%

Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

Zhao

Han

et al. 2020

Mathematical Problems in Engineering

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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.

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Ship Target Detection Algorithm Based on Improved Faster R-CNN

Cited by 65 publications

References 21 publications

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

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