Plankton detection based on improved YOLOv5

Xiaohui Li; Shenming Gu; Dan Wei

doi:10.1117/12.2678647

14 April 2023 Plankton detection based on improved YOLOv5

Xiaohui Li, Shenming Gu, Dan Wei

Proceedings Volume 12634, International Conference on Optics and Machine Vision (ICOMV 2023); 1263407 (2023) https://doi.org/10.1117/12.2678647
Event: International Conference on Optics and Machine Vision (ICOMV 2023), 2023, Changsha, China

Abstract

With the rapid development of marine resource exploitation, planktonic microorganisms have gradually become one of the research directions in the field of machine vision. In order to optimize the detection of small targets in the images of planktonic microorganisms, this paper proposes an improved YOLOv5s model to enhance the detection of planktonic microorganisms. The SE attention mechanism allows the network to pay more attention to the target feature area and suppress useless feature information. The PANet feature module is improved into a weighted bidirectional pyramidal BiFPN feature fusion network to achieve high-efficiency bidirectional cross-scale connectivity and weighted feature map fusion. The results show that the combination of the SE attention mechanism and BiFPN feature fusion improves the mAP value by 6.96%, increases the precision by 11.45%, and reduces the loss rate by 1.62%. Our proposed method effectively solves the problems of false detection, missed detection, and low detection accuracy of the existing models for small target detection.

Citation Download Citation

Xiaohui Li, Shenming Gu, and Dan Wei "Plankton detection based on improved YOLOv5", Proc. SPIE 12634, International Conference on Optics and Machine Vision (ICOMV 2023), 1263407 (14 April 2023); https://doi.org/10.1117/12.2678647

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KEYWORDS

Feature fusion

Detection and tracking algorithms

Target detection

Microorganisms

Education and training

Small targets

Visual process modeling

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