Small target detection based on attention mechanism feature fusion

Abstract

In recent years, the existing target detection algorithms have difficulties in detecting small targets, such as low target resolution, large interference in complex scenes, and lack of large and complete small target detection data sets. To solve the above problems, this paper proposes a new Yolov5 based target detection improvement model E-b-yolov5. This method first preprocesses the data, and then adds ECA to the last layer of Backbone structure by changing the basic network of Yolov5, This lightweight module effectively avoids dimension reduction. Secondly, CIoU is used as the loss function of frame regression to achieve high-precision positioning. In addition, Bifpn (Efficient Det) feature fusion is added to enhance the feature representation ability for small target detection, and the removal of single input side nodes reduces the amount of calculation. Finally, the model is distilled to increase the recall and accuracy of the model. Compared with the original yolov5 method, E-b-yolov5's mAP on the validation subset of the aerial photography dataset VisDrone 2019 DET reached 41%, 8% higher than YOLOv5's benchmark network; On the test subset, mAPreached33.4%, 4.3% higher than Faster R-CNN. research on small object detection to improve the detection effect of small object technology and extend its application scope in practical scenarios.