Page 249 - 《水产学报》2026年第3期

3 期 水 产 学 报 50 卷

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图版 Ⅲ YOLOv11-AMSTAR 与不同模型检测对比
1~7.原始图像，8~14. YOLOv11 的检测结果，15~21. YOLOv11-AMSTAR 的检测结果。
Plate Ⅲ Comparison of YOLOv11-AMSTAR and detection performance with different models
1-7. original image, 8-14. the detection results of YOLOv11, 15-21. the detection results of YOLOv11-AMSTAR.
场在线实时性未能得到很好训练。②模型的泛化 [ 4 ] Kibet D, Shin J H. Counting abalone with high precision using
能力未得到验证，由于数据集样本种类与数量限 YOLOv3 and DeepSORT[J]. Processes, 2023, 11(8): 2351.
制，未能进一步将模型推广到其他海珍类如泥东 [ 5 ] Wang H H, Xing S Y, Zheng Y S, et al. Study on the shape
风 螺 (Babylonia lutosa)、 栉 孔 扇 贝 (Chlamys far- detection method for the precious seafoods based on computer
reri) 等高经济价值小目标监测任务。所以未来的 vision[J]. Agricultural Engineering, 2015, 47(3): 113-120.
研究可以聚焦于水下机器人捕捞移动目标鲍的实 [ 6 ] Li J, Zhang Y, Liu S, et al. Insights into adhesion of abalone: a
际场景，进一步改善模型对移动目标的识别能力， mechanical approach[J]. Journal of the Mechanical Behavior of
提高模型的泛化能力，以适应更复杂的水下多目 Biomedical Materials, 2018, 77: 331-336.
标、多类别实时检测需求，为智慧渔业提供更加 [ 7 ] Chen Y H, Lin D Y, Lan W Y, et al. Underwater measurement
智能、可靠和高效的解决方案。 of abalone shell’s size based on stereo vision[J]. Measurement,
2025, 242: 115699.
（作者声明本文无利益冲突） [ 8 ] Han M, Lyu Z Y, Qiu T, et al. A review on intelligence dehaz-
ing and color restoration for underwater images[J]. IEEE Trans-
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https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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