rate depends greatly on the accurate location of the QR code [4]. At present, machine learning and deep learning methods are commonly used to locate the target [5]. Machine learning and deep learning methods are often applied in the field of automated manufacturing with certain stability [6; 7]. These methods are commonly used for coarse localization of 2D (two-dimensional) codes, but they are deficient in correcting target offset angles or extracting structural information. The traditional QR code localization method determines the location information with the pixel scale feature of three location detection graphs 1:1:3:1:1 [8], which is vulnerable to pixel quality and feature extraction algorithms, with poor stability and a high misclassification rate. On this premise, some scholars have achieved advancements. [PERSON] located the minimum outer rectangle of the outer graph contour and refined the skeleton structure to achieve precise 2D code positioning, and [PERSON] located the minimum outer rectangles of the position detection patterns' contours and refined the skeleton structure to achieve precise 2D code positioning, even though the encoding structure is prone to causing abnormal minimum outer rectangle positioning [9]. [PERSON] achieved QR code correction and localization by extracting the recognition location detection graph's center coordinates for indirect leveling [10]. [PERSON] used the Hough transform to extract the 2D code edge lines, calculated the offset angle, and positioned it [11]. These two methods were less robust with complex contours. However, the randomly stacked parts are partially occluded, and the method of recognizing parts based on weak textures, such as QR codes, may not work. In vision applications, such as face recognition, industrial production, and intelligent driving, detecting occluded targets is a difficult problem. Missing features brought on by the reciprocal occlusion of targets make recognition and localization more difficult [12; 13]. As an important and stable feature for the recognition of occluded target types, contours are the object of intensive research by domestic and foreign scholars. [PERSON] compared the occluded contour fragment with the rest of the spatial contour position to achieve good recognition in the case of large-area occlusions, but the method is only applicable to cases where there are discrete contour fragments of the target [14]. [PERSON] used curvature information to segment contours and measured similarity based on approximating polygons, whereas the algorithm segmentation results are less accurate and more susceptible to occlusions [15]. [PERSON] used the spatial contour location relationship to construct the chord angle feature descriptors of contour sampling points and calculated the matching similarity by an integral graph algorithm, but the method is more complex and is not suitable for industrial production [16]. [PERSON] and [PERSON] described contour features using a shape context, but the algorithm is weak in noise immunity [17; 18]. For localization of parts under local occlusion, [PERSON] obtained features such as hole contours and fitted circle center coordinates for weakly textured workpieces by geometric constraints, but the algorithm suffers from false detection in the case of uneven illumination [19]. [PERSON] created a template using the part corner point feature set, established a geometric hash table, and achieved target matching according to the voting mechanism, but the algorithm efficiency is affected by the number of segmentation points [20]. [PERSON] combined the graphical cutting method with the shape prior model to locate occluded workpieces, whose algorithm accuracy depends on the model segmentation results and applies to regular parts [21].