Detecting characteristics of abnormal body surface in <italic style="font-style: italic">Micropterus salmoides</italic> under recirculating aquaculture systems based on YOLOv8-DBCS

ZHU Ming; WANG Rong; WAN Peng; LEI Xiang; FAN Hao

doi:10.13300/j.cnki.hnlkxb.2026.02.028

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Detecting characteristics of abnormal body surface in Micropterus salmoides under recirculating aquaculture systems based on YOLOv8-DBCS

Food science and agricultural engineering | 更新时间：2026-11-05

- Detecting characteristics of abnormal body surface in Micropterus salmoides under recirculating aquaculture systems based on YOLOv8-DBCS
- Journal of Huazhong Agricultural University Vol. 45, Issue 2, Pages: 269-279(2026)
- 作者机构：
  
  1.华中农业大学工学院/农业农村部水产养殖设施工程重点实验室，武汉 430070
  2.农业农村部长江中下游农业装备重点实验室，武汉 430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.02.028
  CLC： S965.211
- Received：19 July 2025，
  
  Published：15 March 2026
- 稿件说明：
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朱明，汪荣，万鹏，等.基于YOLOv8-DBCS的循环水养殖环境下大口黑鲈异常体表特征检测［J］.华中农业大学学报，2026，45（2）：269‐279.

ZHU Ming,WANG Rong,WAN Peng,et al.Detecting characteristics of abnormal body surface in Micropterus salmoides under recirculating aquaculture systems based on YOLOv8-DBCS[J].Journal of Huazhong Agricultural University,2026,45(02):269-279.
朱明，汪荣，万鹏，等.基于YOLOv8-DBCS的循环水养殖环境下大口黑鲈异常体表特征检测［J］.华中农业大学学报，2026，45（2）：269‐279. DOI： 10.13300/j.cnki.hnlkxb.2026.02.028.

ZHU Ming,WANG Rong,WAN Peng,et al.Detecting characteristics of abnormal body surface in Micropterus salmoides under recirculating aquaculture systems based on YOLOv8-DBCS[J].Journal of Huazhong Agricultural University,2026,45(02):269-279. DOI： 10.13300/j.cnki.hnlkxb.2026.02.028.

摘要

大口黑鲈（

Micropterus salmoides

）在循环水养殖过程中容易感染细菌和病毒，得病早期体表会出现充血和白斑等异常特征。为避免大口黑鲈大规模养殖死亡，提出一种基于YOLOv8的大口黑鲈异常体表特征检测模型YOLOv8-DBCS。首先，基于StarNet网络提出一种动态深度卷积（DIConv）主干网络DIStarNet，DIConv通过设计动态卷积核权重机制自适应调整卷积操作，进而有效捕捉多尺度的特征信息；其次，在颈部网络引入加权双向特征金字塔网络（bi-directional feature pyramid network，BiFPN）增强对来自主干网络多尺度信息的特征融合能力；此外，在检测头前加入CBAM（convolutional block attention module）注意力机制，提升对鱼体异常体表特征图像的学习与预测；最后将目标识别损失函数替换为SIoU（SCYLLA-int

ersection over union），以改善模型预测框与真实框的重合度，进一步提高模型对鱼体异常体表特征识别准确率。结果显示：YOLOv8-DBCS在检测性能上表现优异，YOLOv8-DBCS评价指标准确率（precision）、召回率（recall）、mAP

和mAP

50-95

分别为95.8%、92.4%、97.5%和66.2%；与基线模型相比分别提高3.6、4.9、7.0和3.4百分点。在模型大小上，YOLOv8-DBCS的参数量（parameters）为1.85×10

，与基线模型相比降低了38.5%。

Abstract

Micropterus salmoides

is prone to bacterial and viral infections under recirculating aquaculture systems （RAS）. Characteristics of abnormal body surface in

M. salmoides

including redness and white spots may appear at the early stages of its illness. A YOLOv8-BCS model for detecting the characteristics of abnormal body surface in

M. salmoides

based on YOLOv8 was proposed to prevent large-scale mortality in aquaculture. A dynamic depthwise convolution（DIConv） feature extraction network， DIStarNet， was proposed based on the StarNet network. DIConv adaptively adjusted the convolution operation by designing a dynamic convolution kernel weight mechanism， thereby effectively capturing multi-scale feature information. A weighted bidirectional feature pyramid network（bi-directional feature pyramid network，BiFPN） was introduced into the neck network to enhance the feature fusion capability of multi-scale information from the backbone network. A convolutional block attention module （CBAM） attention mechanism was added before detection to improve the learning and extracting images of the characteristics of abnormal body surface in fish. The target recognition loss function was replaced with SCYLLA-intersection over union （SIoU） to improve the overlap between the model's predicted bounding boxes and the ground truth bounding boxes， thereby further enhancing the accuracy of recognizing the characteristics of abnormal body surface in fish. The results showed that YOLOv8 DBCS exhibited excellent performance of detection， with evaluation metrics including Precision， Recall， mAP

， and mAP

50-95

ing 95.8%， 92.4%， 97.5%， and 66.2%， respectively. Compared with that of the baseline model， these metrics increased by 3.6， 4.9， 7.0， and 3.4 percentage point， respectively. The model size of YOLOv8-DBCS was 1.85 million parameters， with a reduction of 38.5% compared with that of the baseline model. It will provide valuable reference for detecting the characteristics of abnormal body surface and early warning of diseases under recirculating aquaculture systems for

M. salmoides

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Detecting characteristics of abnormal body surface in Micropterus salmoides under recirculating aquaculture systems based on YOLOv8-DBCS

DOI：10.13300/j.cnki.hnlkxb.2026.02.028

摘要

Abstract

关键词

Keywords

references