YOLOv8-SC: an improved seafood target-detection model
Year: 2025
Type: article
Abstract: The advancement of convolutional neural networks has significantly enhanced autonomous underwater fishing. This study introduces YOLOv8-SC, a lightweight model designed for detecting valuable seafood and sea cucumbers in aquatic robots with limited resources. YOLOv8-SC tackles the challenges of high parameter counts and memory usage in large architectures. It replaces the traditional C2f module with the Sequential Optimized Squeeze Excitation (SOSE) module to streamline the structure and reduce parameters. The model adopts a Bi-directional Feature Pyramid Network (BiFPN)-based architecture to improve accuracy without adding detection heads. The AGConv module and Compact Detect detector further reduce computational demands. Soft-NMS enhances detection in complex environments. Achieving a mean average precision (mAP@50) of 83.7% for seafood, YOLOv8-SC demonstrates strong accuracy with significantly lower resource requirements, making it well-suited for underwater detection tasks.
Authors: Zhaofeng Cong, Fusheng Yu
Institution: Shandong Jianzhu University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $1,060
OpenAlex ID: W4411111864