[1]HAN Yuntao,LIU Yupeng,HU Yueming,et al.Target detection of coral bleaching images based on improved Yolov8n[J].CAAI Transactions on Intelligent Systems,2025,20(5):1148-1157.[doi:10.11992/tis.202412019]
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Target detection of coral bleaching images based on improved Yolov8n

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 5 Page number: 1148-1157 Column: 学术论文—机器感知与模式识别 Public date: 2025-09-05
Title:
Target detection of coral bleaching images based on improved Yolov8n
Author(s):
HAN Yuntao1; 2;  LIU Yupeng1;  HU Yueming3;  SUN Baopeng1;  YANG Jiaqi1
1. School of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. Nanhai Innovation and Development Center, Harbin Engineering University, Sanya 572000, China;
3. No.703 Research Institute of CSSC, Harbin 150078, China
Keywords:
Yolov8_CSHC; coral bleaching detection; spatial pyramid pooling network; cascading grouping attention module; CIB_C2f module; hybrid attention Transformer; Marjan balance Dataset
CLC:
TP391.4
DOI:
10.11992/tis.202412019
Abstract:
To address low detection accuracy in coral bleaching images caused by blurred features and complex backgrounds, we propose an improved Yolov8n-based target detection algorithm for coral bleaching, named Yolov8_CSHC. The C2f(concatenated feature fusion) module improved by using compact inverted block (CIB) optimizes model parameters, enabling faster detection. A spatial pyramid pooling network enhanced with local attention mechanisms is introduced to improve detailed feature perception. During feature fusion, a cascaded group attention mechanism increases attention diversity and computational efficiency, allowing the model to rapidly focus on relevant feature areas. Additionally, a hybrid attention transformer module is applied for single-image super-resolution, enhancing semantic information and global perception of small targets. Experimental results demonstrate that, on Marjan balance Dataset, Yolov8_CSHC reduces GFLOPS by 12% and improves mAP@0.5-0.95 by 3.6 percentage points compared with Yolov8n, effectively detecting coral bleaching in complex marine environments.
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