# A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
## 1 Introduction  
Remanufacturing is gaining more attention for its significant impact on establishing a sustainable circular economy [1]. This increase in interest is mainly due to its capacity for resource efficiency and waste reduction. A critical aspect of remanufacturing is the difficulty in aligning production planning and control with remanufacturing processes. [PERSON] [2] extensively discussed the research needs in this domain, highlighting the gap between industry practice and theoretical development. Additionally, the development of a remanufacturing supply chain management system, as exemplified in the case study by [PERSON] and [PERSON] [3], further demonstrates the practical applications and benefits of remanufacturing in the industry. The evolution of remanufacturing processes is further emphasized by [PERSON] et al. [4] and [PERSON] et al. [5], who highlighted the integration of cutting-edge systems like cloud technologies in remanufacturing. One crucial step in remanufacturing involves inspecting end-of-life (EOL) products, which is mostly carried out manually by a skilled worker [6]. However, with promising achievements in computer vision applications, scientists are now focusing on automating this remanufacturing step by adopting intelligent defect detection approaches to recognize faulty regions on EOL parts that require restoration [7; 8; 9]. Defect detection on metal surfaces [10] is an important area of research that is gaining more attention from the scientific community as we move into a sustainable environment. Defects on metal surfaces occur in several types, including cracks, scratches, inclusions, corrosion, spots, patches, etc. [11]. Such defects can affect the quality and performance of the product. [PERSON] et al. [12] conducted an extensive exploration of traditional and deep learning-based methods for surface defect detection, underscoring the evolution from basic feature analysis to sophisticated neural architectures capable of handling complex defect patterns. Furthermore, [PERSON] et al. [13] extended the discussion to the application of machine learning in solid mechanics, suggesting a broader context where these computational models contribute to predictive maintenance and quality assurance in manufacturing processes. Thus, there have been many efforts to develop and adopt different models for efficient and accurate detection of such defects. Some researchers have focused on developing novel deep learning architecture targeting a specific environment and dataset [14; 15; 16; 17; 18]. Others adopted and fine-tuned some of the existing models trained and developed for a general object detection domain [7; 8; 9; 19; 20; 21; 22]. A recent approach involves improving a well-performed existing model utilizing hybrid methods [23; 24; 25; 26]. Therefore, a behavioral study of the state-of-the-art object detection model on detecting defects on metal surfaces is important for further decision support in the selection of the repair process. Thus, this paper focuses on investigating the state-of-the-art object detection model for the defect detection task. Many novel approaches are based on convolutional neural networks (CNNs). CNNs are robust networks used for extracting embedded features for images, such as corners, edges, etc., which makes them widely adopted in the object detection task [27]. [PERSON] et al. [14] developed a detection pipeline based on cascaded autoencoder (CASAE) and CNN networks. This architecture uses CASAE to localize and segment the defect, whereas CNN is used to classify the defect type. Moreover, [PERSON] et al. [15] and [PERSON] et al. [16] designed similar networks that used CNN for feature extraction and a fully connected neural network (FCNN) for the classification step. Both focused on detecting defects in additively manufactured parts, which include cracks, porosity, and lack of fusion. In addition, [PERSON] et al.