A gradient fusion-based image data augmentation method for reflective workpieces detection under small size datasets

Various of Convolutional Neural Network-based object detection models have been widely used in the industrial field. However, the high accuracy of the object detection of these models is difficult to obtain in the industrial sorting line. This is due to the use of small dataset considering of production cost and the changing features of the reflective workpiece. In order to increase the detecting accuracy, a gradient fusion-based image data augmentation method was presented in this paper. It consisted of a high-dynamic range (HDR) exposing algorithm and an image reconstructing algorithm. It augmented the image data for the training and predicting by increasing the feature richness within the regions of reflection and shadow of the image. Tests were conducted on the comparison with other exposing and image fusion methods. The universality of the proposed method was analyzed by testing on various kinds of workpieces and different models including YOLOv8 and SSD. Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) method and Mean Average Precision (mAP) were used to analyze the model performance improvement. The results showed that the proposed data augmentation method improved the feature richness of the image and the accuracy of the object detection for the reflective workpieces under small size datasets.