Artikel ; Online: Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture
Fishes, Vol 8, Iss 169, p
2023 Band 169
Abstract: In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality ... ...
Abstract | In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish detection model, using an improved YOLOV5 network for aquaculture (DFYOLO). The specific implementation methods are as follows: (1) the C3 structure is used instead of the CSPNet structure of the YOLOV5 model to facilitate the industrial deployment of the algorithm; (2) all the 3 × 3 convolutional kernels in the backbone network are replaced by a convolutional kernel group consisting of parallel 3 × 3, 1 × 3 and 3 × 1 convolutional kernels; and (3) the convolutional block attention module is added to the YOLOV5 algorithm. Experimental results in a fishing ground showed that the DFYOLO is better than that of the original YOLOV5 network, and the average precision was improved from 94.52% to 99.38% (when the intersection over union is 0.5), for an increase of 4.86%. Therefore, the DFYOLO network can effectively detect diseased fish and is applicable in intensive aquaculture. |
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Schlagwörter | real-time ; epidemic prevention ; algorithm ; target detection ; convolution kernel group ; Biology (General) ; QH301-705.5 ; Genetics ; QH426-470 |
Thema/Rubrik (Code) | 006 |
Sprache | Englisch |
Erscheinungsdatum | 2023-03-01T00:00:00Z |
Verlag | MDPI AG |
Dokumenttyp | Artikel ; Online |
Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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