# A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White
## 1 Introduction  
In automotive body welding production lines, various visual defects can occur at spot welds, such as edge welds, overlap, cold solder joint and burr. The formation of welding defects is closely related to sheet surface conditions, assembly precision, and welding parameter settings. For example, contaminants or coatings on the sheet surface can alter electrical conductivity and melting behavior, leading to burn-through or insufficient fusion. Poor assembly precision (e.g., excessive gaps or misalignment) affects nugget formation and joint quality. Improper welding parameters (e.g., an excessive current or insufficient welding time) may result in defects such as over-melting or incomplete fusion. The images of spot welds are visually rich and can partially reflect the internal quality of the molten core. These defects are critical because they directly impact the mechanical strength and structural integrity of the welding spots. For instance, incomplete fusion and overlap can lead to weak weld nuggets, reducing load-bearing capacity and fatigue life under cyclic stresses. Burrs and edge welding may introduce stress concentrations, increasing the likelihood of crack initiation. Studies [1, 2, 3] have shown that defective welds often fail mechanical strength tests such as tensile shear and peel tests. Moreover, industry standards such as NQST and AHSS/UHSS welding guidelines set strict criteria to ensure that weld meet the required strength and durability for automotive safety. Welding spot images provide rich and intuitive information, allowing for a quality assessment by analyzing the weld spot's shape, brightness distribution, and surface texture. To ensure a comprehensive evaluation, we referred to the automotive industry standard NQST (Nugget Quality Strength Level) and the welding guidelines for AHSS/UHSS steels established by automobile manufacturers. Currently, the detection of defects on small samples in production lines still relies on manual visual inspection, resulting in low efficiency and delayed feedback. Machine vision-based methods for spot weld quality detection simulate manual visual inspection and offer advantages such as high efficiency, low cost and a high degree of automation. However, due to the complex working conditions in production lines and strong lighting disturbances, the requirements for the accuracy and robustness of visual detection algorithms are quite high. Visual recognition methods can be divided into traditional image processing methods and deep learning methods. Conventional image processing methods often rely on manually created features to describe spot weld quality, including shape, color and texture. Specialized image processing techniques are used to extract these features for detection. Since spot welds are circular, the Hough circle transform [4] is commonly used to extract the spot weld contours. For example, [PERSON] [5] used the Hough circle transformation and multi-contour clustering analysis to locate spot welds with incomplete circular contours. To quickly locate spot weld regions, [PERSON] et al. [6] proposed an angle-assisted circle detection algorithm based on a randomized Hough transform to improve computational efficiency and robustness. Considering the elliptical geometry of spot welds, some researchers [7; 8; 9] calculated the area of the molten core by adjusting the major and minor axes of the spot weld footprints to estimate the spot weld quality. There are some alternative non-destructive testing methods for welding spot detection, such as ultrasonic testing, X-ray testing and thermography. Active thermography is a classical non-destructive testing (NDT) technique widely applied in spot weld inspection due to its ability to detect both surface and subsurface defects. These methods [10; 11] involve applying an external heat source, such as optical, inductive, or ultrasonic excitation, to the weld region, followed by capturing the resulting thermal response using an infrared camera.