Responsibilities • Research the visual characteristics of paint surface defects (e.g., scratches, sags, orange peel, impurities) according to Scania's quality requirements, exploring methods from traditional image processing to deep learning (e.g., segmentation, detection networks). • Set up a simple experimental environment to test algorithms and compare performance on collected paint surface images. • Assist in building a small-scale dataset of paint defect images and participate in formulating annotation standards. Requirements • Current postgraduate or PhD student in Computer Science, Automation, Image Processing, or a related field. • Proficient in Python/C++, with hands-on experience in image processing libraries such as OpenCV and PIL. • Preferred Qualifications: Experience in defect detection, industrial vision, or related projects/competitions; familiarity with tools such as Halcon or VisionPro. • Possess the independent capability to set up optical imaging systems.