Design of Online Vision Detection System for Stator Winding Coil

The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.

A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.

Lineament Patterns and Mineralization Related to Alteration Zone by Using ASAR-ASTER Imagery in Hize Jan-Sharaf Abad Au-Ag Epithermal Mineralized Zone (East Azarbaijan—NW Iran)

East Azarbaijan belongs to the Iranian plateau and is part of lesser Caucasus province. Studied area is located in west-central Alborz. The intrusion of oligocene bodies in various units causes the alteration and mineralization in northwest of Iran. The Hizejan-Sharafabad is one of this named mineralized zone. Granitoidicrocks with component of Granodiorite to alkali have been influenced by hydrothermal fluids. Fractures and faults are as weak zone in earth surface and hydrothermal fluids rise to surface by these geological structures. These solutions cause to alteration in host rocks. Alteration zones are important features for the exploration of deposits. The altered rocks have specific absorption in some spectral portion and ASTER sensor is able to identify the type of alteration. Remote sensing method is useful tool for discovering altered area. The purpose of this study is to appraise ASTER data for surveying altered minerals in Hizejan-Sharafabad area in the event of detecting the potential mineralized areas. In this research, False Color Composite (FCC), Band ratio, and color composite ratio techniques are applied on ASTER data and Silica, Argilic, and Propylitic alteration zones are detected. These alteration types and mineralized area are related to Hizejan–Sharafabad fault which is absent in the fault maps. ASAR image processing has been used for lineaments and faults identified by the aid of Directional and Canny Algorithm filters. The structural study focuses on fracture zones and their characteristics including strike, length, and relationship with alteration zones.