摘要： 高密度聚乙烯（HDPE）是一种新型热塑性渔船材料，其焊接质量是保证HDPE渔船安全的主要影响因素之一。针对HDPE渔船焊缝缺陷检测中存在的缺陷与背景相似度高、小目标特征弱等难题，本研究提出了一种改进的ACA-YOLOv8（Adown-CCFM-AC-mix-YOLOv8）目标检测算法。该方法采用自适应下采样（ADown）策略有效保留缺陷特征，通过跨尺度一致性特征融合网络（CCFM）提升多尺度特征表达能力，并在特征融合过程中引入自注意力与卷积混合（AC-mix）机制增强小目标检测能力。结果显示，改进后的模型在保持轻量化的同时，平均检测精度达到98.9%，较原始模型提升3.2%，参数量减少43.5%，计算量降低2.0G。该算法更能满足工业生产车间HDPE渔船焊缝缺陷检测的设备计算需求。

关键词: 深度学习, 图像处理, 改进的YOLOv8n, 热塑性渔船材料, 缺陷检测

Abstract:  High-density polyethylene (HDPE) is a novel thermoplastic material for fishing vessel construction, whose welding quality is one of the primary factors ensuring the safety of HDPE fishing vessels. To address the challenges in HDPE fishing vessel welds defect detection, including high similarity between defects and background as well as weak small-target features, this study proposes an improved ACA-YOLOv8(Adown-CCFM-AC-mix-YOLOv8) object detection algorithm.The proposed method employs an Adaptive Downsampling(ADown) strategy to effectively preserve defect features, enhances multi-scale feature representation through a Cross-scale Consistent Feature Fusion Network(CCFM), and incorporates a Self-attention and Convolution Mixed(AC-mix) mechanism during feature fusion to improve small target detection capability.Experimental results demonstrate that the improved model maintains lightweight characteristics while achieving an average detection accuracy of 98.9%, representing 3.2%  improvement over the baseline model. Additionally, it reduces parameters by 43.5% and computational load by 2.0G. This algorithm better meets the computational requirements for HDPE fishing vessel welds defect detection in industrial production environments.

Key words: deep learning, image processing, improved YOLOv8n, thermoplastic hull materials, defect detection