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Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

Large gas fields play an important role in natural gas industry. Recovery rate, plateau duration, recovery at the end of plateau, decline rate and recovery factor are the key development indexes for dynamic performance analysis and development planning. Scientific prediction for those indexes can support gas development planning strongly. Through mining statistical analysis of 150 large gas fields and numerical simulation analysis, 23 objective influencing factors which affect the development effect are studied. Gas fields are classified according to the main influencing factors, and then the distribution of development indexes are summarized. Finally, a series of prediction methods for key development indexes are established. Based on the above work, it is found that matrix permeability, drive types, reservoir architecture and fluid type are the most sensitive factors among them. According to the most sensitive factors, gas fields should be divided into 4 categories, and 13 subcategories and the distributions rules of development indexes of all categories are presented. Then new prediction methods for development indexes are established, including linear empirical formula method, similarity analogy prediction method based on Euclidean theorems, and probabilistic values method. In this process, according to the characteristics of influencing factors, logarithmic and piecewise function methods are used for dimensionless treatment, and the prediction accuracy of the methods is improved. Finally, the expert system software is developed which can automatically predict the key development indexes. The prediction accuracy is over 80% which can satisfy the requirement of strategic planning. The new methods have the characteristics of multiple methods, applicable to multiple gas field types and predicting multiple development indexes. Those methods can be applied to predict the development indexes of new fields and evaluate the development effects of matured gas fields in batch.