Detection Algorithm of Small Target in Receptive Field Block

Abstract

The one-stage algorithm SSD (single shot multibox detector) proposed earlier will increase the number of computation channels after 3×3 convolution in the feature extraction of the backbone network. At the same time, these extracted features are directly generated feature maps and respectively thrown into the prediction model, thus causing no good connection of information between layers. In the process of detection, the neural network dominates large targets. Small objects are more likely to be missed, which results in a lower detection rate for small objects. Based on SSD, this paper incorporates a receptive field block based on feature fusion. On the backbone network of feature extraction, the feature fusion module is extracted based on the perceptual visual field feature to enhance the detection effect on small targets. The mean average precision of the improved algorithm framework on the public data of VOC is 81.8%, and the mean average precision on the aerial dataset for the small target is 82.8%. At the expense of part of the speed, the precision has large advantage.

Keywords

• computer vision
• feature fusion
• receptive field; small target
• deep learning