| Popis: |
The current implementation of small object detection algorithms is mainly to design various feature fusion modules. It is difficult to achieve a balance between the detection effect and the model complexity. In addition, compared with regular object, small object has less information and is difficult to extract features. To solve these two problems, a channel attention module is adopted to use a local cross-channels interaction strategy without dimensionality reduction. This module realizes the information association between channels and learns the correlation between features of different channels by considering the weight allocation of features of each channel. In addition, an improved feature fusion module is applied to integrating both the low-level and high-level features for multi-scale object detection. Through such a manner, the accuracy of small object detection is improved. The backbone network adopts ResNet with strong feature expression ability and fast speed, which ensures the convergence of the network while acquiring more network features. The loss function adopts Focal Loss to reduce the weight of easy-to-classify samples, making the model pay more attention to the classification of difficult-to-classify samples during training. The algorithm framework has the mAP of 82.7% on the VOC data set, 86.8% on the aerial photography data set. |
