# A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White
## 5 Conclusions and Future Works  
In conclusion, this paper introduces a novel TA-YOLOv8-based model, termed the Weld Spot Defect Detection Model (WSDDM), for the detection and classification of welding spot defects in car body production. Our proposed model effectively addresses challenges posed by small sample sizes, class imbalance, and noisy data, often encountered in Body-in-White welding spot datasets. The TA-YOLOv8 network employs a Task-Aligned (TA) detection head and a cross-scale feature fusion strategy, which significantly enhances  
Figure 12: Experimental pipeline and integrated detection systems. sensitivity to fine-grained features, facilitating the detection of small, dense welding spots with high accuracy. The experimental results demonstrate that the WSDDM achieves a detection speed improvement of 2.8% over YOLOv8 and an mAP@50-95 increase of 1.35%, along with a 0.1226 rise in the recall rate. Furthermore, the WSDDM's lightweight DarkNet53 backbone enables efficient, accurate real-time processing suitable for industrial applications. A comparative analysis shows that while Faster R-CNN achieved the highest detection accuracy, it came at a substantial time cost, whereas the WSDDM maintained high detection precision at a fraction of the computational expense. The model's robustness across varying weld spot shapes and lighting conditions underscores its generalization capability, making it a viable and reliable tool for automated welding spot quality inspection in real-world production environments. To address the issue of the loss of feature information for spot welding during feed forward convolution computations, this paper improves the YOLOv8 model from the perspective of the backbone network, aiming to enhance the detection accuracy and speed of the small-target welding spot. A defective welding spot generation model is established based on autoencoder initialization and the theory of balanced generative adversarial networks, facilitating accurate data augmentation in imbalanced datasets. Finally, the effectiveness of the proposed method is validated using an image dataset collected from the field. The main conclusions are as follows:  
1. The proposed small-target solder joint detection model outperforms several typical object detection algorithms in terms of mean Average Precision (mAP) and detection speed. The cross-layer feature fusion structure effectively integrates and refines multi-scale features, utilizing robust low-level features to improve the detection accuracy of small-scale targets. The lightweight network DarkNet53 enhances inference speed while maintaining accuracy. 2. Qualitative and quantitative comparative analysis with typical GAN models indicates that the proposed defect welding spot generation model can produce high-quality and diverse defect images in highly imbalanced datasets. 3. The ablation experiment results demonstrate that the Task-Aligned method, data augmentation, and feature pre-training all contribute to the deep learning model's ability to learn discriminative features from solder joint images. For the common weld spots quality categories on the white body production line, the performance of the proposed TA-YOLOv8 model generally surpasses that of other foundational visual models. In deep visual models, false positives (FPs) and false negatives (FNs) are common issues when evaluating performance. To analyze and explain the model's failure in such cases, a systematic experimental analysis is often required. The following are steps and methods for analyzing and explaining the reasons behind false positives and false negatives, including Error Classification Analysis, Model Output Analysis and case studies. By following these steps, we are able to conduct an in-depth analysis of false positives and false negatives in the model, understand their root causes, and improve the model's performance.