Abstract: Objective: To address the shortcomings of the current switch machine gap image detection system on Qingdao Metro Line 13, an edge detection optimization algorithm based on quantity counts is proposed to improve the accuracy of gap detection.Method: Median filtering is used to filter out the noise in the collected gap image and preserve the edge information of the image. Image morphology is used to process the corrosion, dilation, open operation, and close operation in the basic algorithm to further optimize the image edges, avoid blurring or roughness of the image edge contour, and provide highquality image samples for subsequent processing. Canny operator is used to extract the edge features of the gap image, updating the threshold in realtime by combining dynamic threshold algorithm. Through the edge detection optimization algorithm based on quantity counts, the final edge position of the image is confirmed, thus the gap value is calculated.Result & Conclusion: After experimental verification, the accuracy of the image gap optimized by the algorithm is improved by nearly 5%, with a significant improvement in accuracy rate. Therefore, edge detection optimization algorithms based on quantity counts can reduce the probability of switch machine gap data alarms caused by inaccurate detection.