Wear particle analysis is an effective method for equipment wear fault diagnosis and prediction. In order to improve the automation and intelligence degree of wear particle detection, a target detection algorithm based on improved YOLOv4 was proposed to automatically extract and identify target particles from wear particle images with complex backgrounds. It overcame the error caused by the traditional method of image segmentation in the face of multi-wear particle images, and was applied to wear particle image recognition in aeroengine Scanning Electron Microscope (SEM). Firstly, VoVNetv2-39 was used to replace YOLOv4’s original backbone network CSPDarknet53. Its unique improved OSA module guaranteed the gradient and direction propagation without interference, and maintained channel information while owning deeper network depth, thus improved the network performance. Secondly, BiFPN feature pyramid structure was introduced to connect with the new backbone. BiFPN could not only completed the feature extraction from top to bottom, but also achieved the weighted fusion of features with different resolutions from bottom to top. At the same time, BiFPN increased the horizontal connection between the input and output of the same level, enriching the semantic information of the feature map. Finally, all 3×3 standard convolutions in the model were adjusted by depthwise separable convolution, in order to build a lightweight network.

The improved YOLOv4’s network architecture for image target detection consist of three parts. The first part was the new backbone feature extraction network—VoVNetv2-39. It preliminarily extracted the feature of wear particle images in SEM with the input size of 416×416×3. Three initial effective feature layers were obtained by convolution, combination and addition. The second part was the strengthen feature extraction network—SPP and BiFPN. The first feature layer was passed into BiFPN after OSA Module Stage3 and a convolution block. The second feature layer was passed into BiFPN after OSA Module Stage4 and a convolution block. The third feature layer was passed into Spatial Pyramid Pooling (SPP) after OSA module Stage5 and three convolution blocks. SPP used pooling cores with different scales of 13×13, 9×9, 5×5 and 1×1 for maximum pooling and then merged. The merged results were passed into BiFPN which realized feature fusion of the three initial effective feature layers to extract more effective features. The third part was the prediction network-YOLO Head. The feature map was divided into three kinds of grids with different numbers of 52×52, 26×26 and 13×13. The prior frames of different sizes were generated on each grid. The classification, confidence and coordinates of the wear particles in SEM images were predicted by the prior frames, and then the prediction results were output.

In the experimental stage, firstly, wear particle images in SEM with typical wear characteristics were selected. The data set was expanded by image data enhancement method. The training set, the validation set and the test set were divided to form the self-built SEM wear particle image set. Secondly, the size of all images was unified as 416×416×3, and then these images were input into the improved YOLOv4’s network. Thirdly, the transfer learning was used to solve the problem of the small number of wear particle images in SEM. The IamgeNet data set was used to pre-train the new model, and the freezing idea was used to accelerate the model training process and obtain the initial weight and deviation of the model. The training parameters were set, and the self-built wear particle image set in SEM was used to start freezing training. In this period, only some parameters of the network were fine-tuned. The unfreezing preparing training began after the freezing training. In this period, the weight and deviation of the model could be updated. Then, the output mAP values were compared to obtain the optimal model. Finally, the actual wear particle images in aeroengine SEM were used to verify. The results showed that, compared with the original YOLOv4 network, the number of the new network’s parameters was significantly reduced and the recognition speed was improved by 51.1% on the premise of ensuring accuracy. It could meet the detection requirements of fast, simple and high precision of actual SEM wear particle images, and had the potential engineering application value.