# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
###### Abstract  
The rapid advancement of industrial automation in the automotive manufacturing sector has heightened demand for welding quality, particularly in critical component welding, where traditional manual inspection methods are inefficient and prone to human error, leading to low defect recognition rates that fail to meet modern manufacturing standards. To address these challenges, an enhanced YOLOv8-based algorithm for steel defect detection, termed YOLOv8-WD (weld detection), was developed to improve accuracy and efficiency in identifying defects in steel. We implemented a novel data augmentation strategy with various image transformation techniques to enhance the model's generalization across different welding scenarios. The Efficient Vision Transformer (EfficientViT) architecture was adopted to optimize feature representation and contextual understanding, improving detection accuracy. Additionally, we integrated the Convolution and Attention Fusion Module (CAFM) to effectively combine local and global features, enhancing the model's ability to capture diverse feature scales. Dynamic convolution (DyConv) techniques were also employed to generate convolutional kernels based on input images, increasing model flexibility and efficiency. Through comprehensive optimization and tuning, our research achieved a mean average precision (map) at IoU 0.5 of 90.5% across multiple datasets, contributing to improved weld defect detection and offering a reliable automated inspection solution for the industry. l 2020 20  
parameter selection and may struggle with noise and varying lighting conditions [8; 9; 10]. This paper explores defect detection technologies for brake joint weld seams, emphasizing their significance in automotive manufacturing and proposing optimized strategies to enhance brake system safety and reliability. Through this study, we aim to offer the automotive industry more reliable and efficient quality monitoring methods, ensuring the safety of drivers and passengers. In recent years, weld defect detection technologies have undergone substantial advancements, driven by researchers integrating advanced techniques to overcome the limitations of traditional methods. For instance, [PERSON] et al. [11] employed laser-generated ultrasound and electromagnetic ultrasonic transducers to evaluate weld seams, utilizing various mother wavelets through discrete wavelet transform statistical methods to extract relevant defect features. [PERSON] et al. [12] designed a wall-climbing robot with a cross-structured optical sensor for weld seam detection, enabling the acquisition of three-dimensional information about the welds. Addressing the complexities and subtleties of defects recorded in X-ray images, [PERSON] et al. [13] combined thermal infrared imaging sensors with industrial robots and developed an image detection algorithm that transforms input RGB thermal images into single-channel grayscale images. This transformation is followed by adaptive thresholding for binarization, effectively revealing the shape and location of defects. [PERSON] [14] proposed the application of eddy current testing for the detection of micro-gap weld joints, establishing a magnetic dipole model for defects, which was validated against the grayscale values of pit magneto-optical images, demonstrating the superiority of finite element analysis over the magnetic dipole model. [PERSON] [15] focused on typical surface welding defects in the pulse GTAW of aluminum alloys, proposing an algorithm that computes local gray probabilities in regions of interest for monitoring welding defects, thus enhancing the real-time and intelligent capabilities of robotic welding. [PERSON] [16] proposed a compressive sensing-based full matrix capture (FMC) data compression method for the phased array ultrasonic technique (PAUT) of nozzle welds.