Aiming at the problems of complex operation and high cost of traditional contact measurement of bearing size in industrial environment, an improved edge detection algorithm for HED network is proposed by adding attention mechanism and Canny algorithm. The method is based on the HED network, the convolutional layer of the fourth and fifth stages of the backbone network is replaced with a continuous hollow convolutional layer, and the pooling step of the third and fourth layers of the network is set to 1, which increases the model receptive field and improves the output edge image accuracy. The efficient channel attention mechanism ECA module is added to effectively suppress the influence of irrelevant texture features and non-edge pixels. Using the non-maximum value suppression and double threshold processing algorithms in Canny algorithm, the coarse edges detected are refined to obtain more accurate bearing edges. The inner and outer diameter parameters of bearing are obtained by using the least square circle fitting method. The experimental results show that the improved HED model achieves 0.811 and 0.833 respectively in ODS and OIS, which can effectively realize the bearing edge detection and ensure the bearing size measurement accuracy.