Object Detection Algorithm Based on Deformable Convolutional Networks for Underwater Images

Zhang, Dong; Lan, Li; Zhu, Zizhong; Jin, Shangang; Gao, Weizhe; Li, Ce

doi:10.1109/cchi.2019.8901912

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…With the aid of removing color aberration and enhancing contrast of underwater images, Zhang et al [70] presented an underwater image saliency detection framework by using deformable convolutional networks. Softmax was applied to regress the feature information of each salient object so that the spatial location could be estimated.…”

Section: Underwater Image Saliency Detectionmentioning

confidence: 99%

Underwater image processing and analysis: A review

Jian

Liu

Luo

et al. 2021

Signal Processing: Image Communication

142

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Section: Underwater Image Saliency Detectionmentioning

confidence: 99%

Underwater image processing and analysis: A review

Jian

Liu

Luo

et al. 2021

Signal Processing: Image Communication

142

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“…Underwater Robot Picking Contest in 2018 provided an underwater object target detection dataset, including sea urchins, sea cucumbers, scallops, and starfish. To test the detection effect of different algorithms, Zhang et al 71 tested the regular faster R-CNN, FPN, and R-FCN. They also tested faster R-CNN and R-FCN with deformable convolution.…”

Section: Different Algorithm Under the Same Datasetmentioning

confidence: 99%

Underwater target recognition methods based on the framework of deep learning: A survey

Teng

Zhao

2020

International Journal of Advanced Robotic Systems

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The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.

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“…By comparing a local region's differences from other areas in an image, these attributes were utilized to assess a region's relative importance. Zhang et al (2019), proposed a pipelined model for detecting salient objects in underwater photos, using deformable convolutional networks. Using the CNN approach, scientists first reduced noise in underwater photographs to improve contrast.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

et al. 2023

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ObjectiveDuring the last few years, underwater object detection and marine resource utilization have gained significant attention from researchers and become active research hotspots in underwater image processing and analysis domains. This research study presents a data fusion-based method for underwater salient object detection and ocean environment monitoring by utilizing a deep model.MethodologyA hybrid model consists of an upgraded AlexNet with Inception v-4 for salient object detection and ocean environment monitoring. For the categorization of spatial data, AlexNet is utilized, whereas Inception V-4 is employed for temporal data (environment monitoring). Moreover, we used preprocessing techniques before the classification task for underwater image enhancement, segmentation, noise and fog removal, restoration, and color constancy.ConclusionThe Real-Time Underwater Image Enhancement (RUIE) dataset and the Marine Underwater Environment Database (MUED) dataset are used in this research project’s data fusion and experimental activities, respectively. Root mean square error (RMSE), computing usage, and accuracy are used to construct the model’s simulation results. The suggested model’s relevance form optimization and conspicuous item prediction issues in the seas is illustrated by the greatest accuracy of 95.7% and low RMSE value of 49 when compared to other baseline models.

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Object Detection Algorithm Based on Deformable Convolutional Networks for Underwater Images

Cited by 8 publications

References 10 publications

Underwater image processing and analysis: A review

Underwater image processing and analysis: A review

Underwater target recognition methods based on the framework of deep learning: A survey

Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

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