Improved Faster R-CNN for Dense Small Objects
Li Chen, Naimeng Cang, Hao Jiang, Shuang Wang · 2022
Despite the great success of general-purpose object detectors in recent years, the detection performance for small objects is not yet satisfactory. Since small objects and large objects have different sensory field sensitivity and small objects can extract fewer features, there is still great potential for development in the field of small object detection. In this paper, we propose an improved algorithm based on Faster-RCNN for dense small object detection in complex environments. Slicing Aided Hyper Inference (SAHI) is used to improve the small object detection task. To optimize accuracy and computational effort, convolution is replaced by an involution operator and is integrated into the backbone of the network. Then, a novel label assignment strategy based on gaussian receptive field is introduced in the Faster-RCNN algorithm to better assign labels. Experiments were conducted on the VisDrone-DET2019 dataset, and the results show that the proposed algorithm is quite an improvement over the original one. The precision and recall of small objects are improved to 24.6% and 38.3%, respectively.