# YOLOv8-WD: Deep Learning-Based Detection of Defects in Automotive Brake Joint Laser Welds
###### Abstract
[PERSON] [17] proposed a compressive sensing-based full matrix capture (FMC) data compression method to address the issues of excessive signal acquisition, storage, and transmission data volume in nozzle weld defect monitoring. [PERSON] [18] proposed a compressive sensing-based weld defect detection method aimed at addressing defects in submerged arc welds. Through continuous advancements, welding defect detection technology is progressing towards higher precision, automation, intelligence, and real-time monitoring. This evolution significantly enhances the efficiency and reliability of welding quality assessments across various industries. Traditional methods have relied on manually designed feature extraction and machine vision classifiers; however, these approaches are subject to human intervention, leading to subjective biases that may result in missed or redundant detections [19]. With advancements in computer technology, deep learning methods have increasingly been applied to weld defect detection. Unlike traditional algorithms, convolutional neural networks (CNNs) automatically extract features, facilitating feature selection and classification while avoiding the pitfalls of manual methods [20]. The YOLO (You Only Look Once) algorithm, introduced by [PERSON] et al. in 2015 through the paper titled \"You Only Look Once: Unified, Real-Time Object Detection,\" has seen multiple updates from version V1 to V10 since 2016, significantly improving detection accuracy while maintaining the speed of single-stage algorithms, making it widely used in engineering inspection applications [21; 22]. For instance, [PERSON] [23] proposed a comprehensive control system based on YOLOv3 for detecting weld defects in hot water tank connection pipes, utilizing a 13-layer Darknet-13 feature extractor that generates predictions at two scales, achieving high accuracy in identifying and localizing welding anomalies. [PERSON] [24] introduced an improved YOLO-based method for detecting-weld regions, addressing low recognition rates in the welding sections by employing image inversion, k-nearest median filtering, CLAHE image enhancement, and gamma correction to enhance image contrast and improve detection accuracy. [PERSON] [25] et al. developed an enhanced YOLOv5 detection network by incorporating a RepVGG module and a Normalized Attention Module (NAM), optimizing network structure to enhance detection speed and the network's sensitivity to feature points. [PERSON] [26] proposed the YOLOv5-IMPROVEMENT model, which integrates a CA attention mechanism, SIOU loss function, and FReLU activation function to enhance detection capabilities for small targets and capture low-sensitivity spatial information. [PERSON] et al. [27] introduced a BOT module to extract. Global information from road damage images, accommodating the large span characteristics of crack targets, incorporated a large separable kernel attention (LKSA) mechanism to improve detection accuracy. Additionally, a CZf Ghost block was constructed in the neck network to reduce computational load while enhancing feature extraction for complex road damage. While these advancements have significantly improved detection accuracy and speed, as we approach 2025, YOLO still faces challenges in specialized tasks, however, such as weld seam defect detection. These include limited adaptability to variations in size, shape, and texture, difficulty capturing long-range dependencies for detecting small or dispersed defects, and sensitivity to noise and low resolution, leading to inaccurate defect identification under diverse conditions. To solve these problems, in this paper, we design a more effective weld defect detection algorithm by employing a multi-level and multi-scale attention mechanism, enhancing the model's ability to capture fine-grained details and distinguish subtle defect features from complex backgrounds, thereby improving detection robustness and accuracy in challenging welding scenarios.