The efficient separation of coal and gangue remains a critical challenge in modern coal mining, directly impacting energy efficiency, environmental protection, and sustainable development. Current machine vision-based sorting methods face significant challenges in dense scenes, where label rewriting problems severely affect model performance, particularly when coal and gangue are closely distributed in conveyor belt images. This paper introduces CGDet (Coal and Gangue Detection), a novel compact convolutional neural network that addresses these challenges through two key innovations. First, we proposed an Object Distribution Density Measurement (ODDM) method to quantitatively analyze the distribution density of coal and gangue, enabling optimal selection of input and feature map resolutions to mitigate label rewriting issues. Second, we developed a Relative Resolution Object Scale Measurement (RROSM) method to assess object scales, guiding the design of a streamlined feature fusion structure that eliminates redundant components while maintaining detection accuracy. Experimental results demonstrate the effectiveness of our approach; CGDet achieved superior performance with AP50 and AR50 scores of 96.7% and 99.2% respectively, while reducing model parameters by 46.76%, computational cost by 47.94%, and inference time by 31.50% compared to traditional models. These improvements make CGDet particularly suitable for real-time coal and gangue sorting in underground mining environments, where computational resources are limited but high accuracy is essential. Our work provides a new perspective on designing compact yet high-performance object detection networks for dense scene applications.

With the aim of solving the problems of high cost, complex maintenance, and low efficiency of existing gear detection technology, a new three-dimensional (3D) full-field optical measurement method for gears is proposed. As an example, the method is verified in the measurement of the 3D error of the gear surface and the gear parameter error of a non-standard right-hand helical gear with a modulus of 1.75 and a number of teeth of 21. The gear accuracy standard GB/T 10095.1-2008 is used to analyze and evaluate the surface 3D error and parameter error measurement results. Based on the results of error analysis, the gear manufacturing improvement measures are analysed and fed back to the gear manufacturing system to realise closed-loop gear processing control and detection in order to produce gear products with higher precision. Our experimental results show that the proposed method can be used to measure the 3D errors at the surface in the X, Y, and Z directions and the parameter errors. The minimum surface error of the gear in the Z-direction of the keyway is −7.24162E-06mm, and the pitch error reaches the allowable value of deviation for a grade 8 precision gear, as set out in the GB/T 10095.1–2008 accuracy standard. A verification process is carried out to show that our method can meet the requirements of modern industrial intermediate precision gear testing.

Straight bevel gears are widely used in mining equipment, ships, heavy industrial equipment, and other fields due to their high capacity and robust transmission. Accurate measurements are essential in order to determine the quality of bevel gears. We propose a method for measuring the accuracy of the top surface profile of the straight bevel gear teeth based on binocular visual technology, computer graphics, error theory, and statistical calculations. In our method, multiple measurement circles are established at equal intervals from the small end of the top surface of the gear tooth to the large end, and the coordinates of the intersection points of these circles with the tooth top edge lines of the gear teeth are extracted. The coordinates of these intersections are fitted to the top surface of the tooth based on NURBS surface theory. The surface profile error between the fitted top surface of the tooth and the designed surface is measured and determined based on the product use requirements, and if this is less than a given threshold, the product is acceptable. With a module of 5 and an eight-level precision, such as the straight bevel gear, the minimum surface profile error measured was −0.0026 mm. These results demonstrate that our method can be used to measure surface profile errors in the straight bevel gears, which will broaden the field of in-depth measurements for the straight bevel gears.

Performance of gear transmissions affects the performance of mechanical equipment. It is necessary to develop more reliable gear pitch and tooth thickness accuracy detection methods in order to evaluate gear transmission performance and detect gear pitch and tooth thickness accuracy more accurately. Based on the basic theory of gears, binocular vision technology, and statistical principles, a new method that measures gear pitch and tooth thickness using machine vision is proposed: coaxial multi-ring detection (MCD). There is no contact, no damage, and a high degree of efficiency with this method. Using this method, we are able to detect the pitch and thickness accuracy of each gear tooth multiple times within the tooth width range and in multiple directions perpendicular to the gear axis. We statistically analyze the measurement results to determine the gear’s most accurate detection results. The measurement method for gear machining accuracy is investigated using the coaxial multi-ring detection method. The statistical analysis of multiple measurement results is carried out, and the measurement results obtained are highly consistent with those of the gear detection center. In conclusion, the measurement results of this method are highly reliable, and they can be used as a reliable basis for evaluating gear transmission performance.

Optical measuring technology is widely used in the tobacco industry, not only for quality inspection of tobacco processing, but also for detecting defects in key mechanical components of tobacco processing equipment. Tobacco production requires the use of optical inspection technology to ensure quality. Optical precision measuring technology will therefore remain a research hotspot and focus in the future. In this paper, we first introduce the history of development and the characteristics of optical precision measurement technology. We then review the principle of measurement, key issues, and technical improvements over recent years in terms of structured light, binocular stereo vision, and digital speckle, as well as their applications in engineering. The calibration algorithm and measurement algorithm of structured light measurement technology need to be improved, and the optimization of the calibration technology and stereo matching technology of binocular stereo vision measurement needs to be improved urgently. How does the digital speckle correlation method adapt to a stronger measurement environment? Finally, we suggest a direction for further research that is worthy of further investigation based on the existing research.