A GAN-based small target detection model
Jianbo Fu, Zhuang Chen · Third International Conference on Computer Vision and Data Mining (ICCVDM 2022) · 2023
Small objects have low content in the image, insignificant features, and are easily disturbed by noise, so that fewer features can be used for target detection. However, the Faster R-CNN object detection model based on deep convolutional neural network undergoes multiple pooling operations during feature extraction, which makes it more difficult to extract the features of small objects effectively, which is unfavorable for the detection of small objects. Aiming at this problem, this paper proposes a Faster R-CNN-based small object detection model that uses GAN to enhance the small object feature expression. First, based on the strong ability of deep feature extraction of Resnet152, Resnet152 is used to replace VGG16 in the original Faster R-CNN; secondly, the GAN model is trained by using appropriate high-resolution object features as the supervision signal of the generation network; finally, set the threshold of small object, so that the object features larger than the threshold directly enter the detection network and reduce the complexity of the model.