Experimental Research on Overwater and Underwater Visual Image Stitching and Fusion Technology of Offshore Operation and Maintenance of Unmanned Ship

Chen, Yuanming; Hong, Xiaobin; Chen, Weiguo; Wang, Hong; Fan, Tianhui

doi:10.3390/jmse10060747

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…The image becomes more in line with the human subjective perception [16]. Underwater image enhancement algorithms can be divided into 2 categories according to the domain of action: the former focuses on the improvement of image pixels, and the latter transforms the image into the frequency domain through the null-frequency transform, where some special properties of the image in the frequency domain are manipulated [17][18][19].…”

Section: Underwater Image Enhancementmentioning

confidence: 99%

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Wang,

Liu,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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Due to the refraction, reflection and absorption of light induced by seawater and suspended particles, the image recognition of underwater vehicle has the problem of low accuracy and difficult localization, and to solve this one problem, an improved LSTM classification algorithm (CH-LSTM) based on CLAHE and HOG is given. First, the improved CLAHE algorithm is used to enhance image by image noise removal and de-fogging, the problems of underwater image distortion, fine lines, and abrupt changes are solved in the aquaculture waters image. Then, the Histogram of Orientation Gradient (HOG) operator is used for feature extraction to describe the shape of sea cucumbers and generate feature vectors; finally, the LSTM algorithm is used to classify the feature vectors and avoid overfitting through memory gates, so the generalization ability of the model was enhanced. The identification and sorting experiments of underwater fishing sea cucumbers show that the proposed algorithm is superior, especially in the environment of poor water quality, and the identification and localization accuracy of sea cucumbers is improved compared with the traditional ground SVM and BP neural network algorithms, and the identification accuracy is above 95.28%.

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Section: Underwater Image Enhancementmentioning

confidence: 99%

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Wang,

Liu,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

“…5 Compute the mean and covariance. Re-substitute the new sigma point set into observation Equation (11) to obtain the predicted observation value, and obtain the predicted mean and covariance of the system through the weighted summation of Equation (12).…”

Section: Target Tracking Based On Ukf Algorithmmentioning

confidence: 99%

“…In recent years, in order to reduce the incidence of ship accidents caused by human factors and improve the safety of ship navigation, intelligent ship technology has developed rapidly, and ship target detection based on cameras has attracted more and more attention [8][9][10][11][12]. Ship target detection is an important part of the autonomous navigation of ships [13].…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Ship Target Detection and Tracking Method Based on Camera in Complex Scenes

Hong

Cui

Chen

et al. 2022

JMSE

Self Cite

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Aiming at the problem that multi-ship target detection and tracking based on cameras is difficult to meet the accuracy and speed requirements at the same time in some complex scenes, an improved YOLOv4 algorithm is proposed, which simplified the network of the feature extraction layer to obtain more shallow feature information and avoid the disappearance of small ship target features, and uses the residual network to replace the continuous convolution operation to solve the problems of network degradation and gradient disappearance. In addition, a nonlinear target tracking model based on the UKF method is constructed to solve the problem of low real-time performance and low precision in multi-ship target tracking. Multi-ship target detection and tracking experiments were carried out in many scenes with large differences in ship sizes, strong background interference, tilted images, backlight, insufficient illumination, and rain. Experimental results show that the average precision of the detection algorithm of this paper is 0.945, and the processing speed is about 34.5 frame per second, where the real-time performance is much better than other algorithms while maintaining high precision. Furthermore, the multiple object tracking accuracy (MOTA) and the multiple object tracking precision (MOTP) of this paper algorithm are 76.4 and 80.6, respectively, which are both better than other algorithms. The method proposed in this paper can realize the ship target detection and tracking well, with less missing detection and false detection, and also has good accuracy and real-time performance. The experimental results provide a valuable theoretical reference for the further practical application of the method.

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“…In recent years, image stitching technology has been widely used in the military, agriculture, geological exploration and other fields [ 4 , 5 ], and it has gradually become an important branch of image processing [ 6 , 7 , 8 ]. Image matching is at the core of image stitching [ 9 , 10 ], and its essence is to calculate the geometric transformation relationship between two images using overlapping areas to obtain the rotation, scale, displacement and other parameter values, and then realize the mosaic of the overlapping areas [ 11 , 12 ]. At present, the mainstream image matching method is the feature-based matching method [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

Deng

Song²,

Han³

et al. 2023

Sensors

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Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.

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Experimental Research on Overwater and Underwater Visual Image Stitching and Fusion Technology of Offshore Operation and Maintenance of Unmanned Ship

Cited by 6 publications

References 32 publications

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Research on Multi-Ship Target Detection and Tracking Method Based on Camera in Complex Scenes

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

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