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<p>Casting defects on the surface of automotive engine blocks directly affect engine performance and service life, making rapid and accurate detection of these tiny defects crucial for improving automotive manufacturing quality. To overcome the limitations of traditional detection methods, such as low efficiency, heavy reliance on manual inspection, and frequent missed or false detections, this study proposes an improved YOLOv8-based automatic detection method for tiny defects on engine block surfaces, named YOLO-CFAEW. The proposed model integrates several key innovations: the introduction of a convolutional attention module (CBAM) into the YOLOv8 backbone to enhance feature extraction in both spatial and channel dimensions, and the design of the Receptive Field Attention Convolution (RFAConv) module, which dynamically adjusts the receptive field to better capture subtle defect features. Additionally, an improved multi-head detection structure is employed to optimize detection heads for defects at various scales, significantly improving the accuracy for tiny defects. A novel Wasserstein distance loss function is utilized to optimize the bounding box regression process, enhancing localization stability and accuracy of small defects. To balance detection accuracy and inference speed, the conventional coupled detection head in YOLOv8 is replaced with a decoupled detection head integrated with a lightweight GiraffeDet network structure. Experimental results demonstrate that the proposed YOLO-CFAEW model achieves outstanding performance on the expanded engine block defect dataset, with mAP@0.5 improved by 17.3% and mAP@0.5:0.95 improved by 9.7% compared to the original YOLOv8 model. The proposed method effectively identifies tiny defects such as blowholes with lower false and missed detection rates, providing an efficient, accurate, and practical solution suitable for real-world industrial applications, with significant potential for improving automotive manufacturing quality.</p>

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YOLO-CFAEW: A YOLOv8-based detection network with CBAM attention, RFAConv module, and multi-head structure for engine casting defect detection

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Aoxue Sun,
  • Zhizhong Mao

摘要

Casting defects on the surface of automotive engine blocks directly affect engine performance and service life, making rapid and accurate detection of these tiny defects crucial for improving automotive manufacturing quality. To overcome the limitations of traditional detection methods, such as low efficiency, heavy reliance on manual inspection, and frequent missed or false detections, this study proposes an improved YOLOv8-based automatic detection method for tiny defects on engine block surfaces, named YOLO-CFAEW. The proposed model integrates several key innovations: the introduction of a convolutional attention module (CBAM) into the YOLOv8 backbone to enhance feature extraction in both spatial and channel dimensions, and the design of the Receptive Field Attention Convolution (RFAConv) module, which dynamically adjusts the receptive field to better capture subtle defect features. Additionally, an improved multi-head detection structure is employed to optimize detection heads for defects at various scales, significantly improving the accuracy for tiny defects. A novel Wasserstein distance loss function is utilized to optimize the bounding box regression process, enhancing localization stability and accuracy of small defects. To balance detection accuracy and inference speed, the conventional coupled detection head in YOLOv8 is replaced with a decoupled detection head integrated with a lightweight GiraffeDet network structure. Experimental results demonstrate that the proposed YOLO-CFAEW model achieves outstanding performance on the expanded engine block defect dataset, with mAP@0.5 improved by 17.3% and mAP@0.5:0.95 improved by 9.7% compared to the original YOLOv8 model. The proposed method effectively identifies tiny defects such as blowholes with lower false and missed detection rates, providing an efficient, accurate, and practical solution suitable for real-world industrial applications, with significant potential for improving automotive manufacturing quality.