A Scene Perception Method Based on MobileNetV3 for Bionic Robotic Fish

Wang, Ming; Du, Xiaobin; Chang, Zheng; Wang, Kunlun

doi:10.1007/978-981-19-6135-9_30

Cited by 1 publication

(2 citation statements)

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“…(5) Multiple object tracking precision (MOTP), which represents the overlap rate between the bounding box of the tracking target and the real bounding box of the target. The greater the value, the greater the accuracy, as shown in Equation (19).…”

Section: Multi-target Tracking Evaluation Indexmentioning

confidence: 99%

“…Cheng [18] constructed a two‐dimensional video‐tracking algorithm based on ConvNet in the automatic framework of fish swarm multi‐target tracking based on iterative training set generation and network training. Chang [19] improved the K‐means clustering method to determine the anchor frame, and optimized the training of the detection model for different hyperparameters. The multi‐threading method was used for optimization and improvement, and the DeepSORT algorithm was used to achieve the goal of bionic robotic fish.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐target detection and tracking of shallow marine organisms based on improved YOLO v5 and DeepSORT

Liu,

An,

Chen

et al. 2024

IET Image Processing

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In order to solve the related problems of detection and tracking of shallow marine organisms, this paper designs a YOLO v5 multi‐target detection and tracking algorithm with attention mechanism. When working underwater, the authors usually encounter many difficulties. Different luminosity and complex coral background usually affect the detection of marine organisms. At the same time, the unrestricted movement of marine organisms, the ability to hide behind rocks and algae, and their mutual occlusion while swimming pose additional challenges to this task. Considering the characteristics of shallow marine organisms activity environment, the attention mechanism is added to the feature extraction network of YOLO v5 to reduce redundant information and improve the detection accuracy of shallow marine organism targets in complex environment. The improved algorithm improves the average detection accuracy of marine organisms target detection by 3.2%. Aiming at the problem of shallow marine organisms target tracking, a shallow marine organisms multi‐target tracking algorithm based on improved Deep Simple Online And Realtime Tracking (SORT) is designed. The improved YOLO v5 algorithm is used to replace Faster R‐CNN (Faster Region‐Convolutional Neural Networks) as the detector of DeepSORT tracking algorithm, and the cascade matching strategy is adopted to solve the problem that the target cannot be tracked continuously when it is occluded for a long time. The experimental results show that the shallow marine organisms multi‐target tracking algorithm based on improved DeepSORT reduces the number of id transformation of marine biological target tracking in shallow sea environment, and improves the accuracy of shallow marine organisms multi‐target tracking.

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Section: Multi-target Tracking Evaluation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%