A Novel Deep Convolutional Neural Network Pooling Algorithm for Small floating objects detection

Abstract

The problem of floating debris in rivers and oceans is growing. To clean floating objects on the water more effectively, IoT-based unmanned boats were chosen for autonomous cleaning. However, the strong light reflections of riverside objects on the water surface pose challenges for vision-based object detection systems to detect small targets. By modifying the pooling module in Spatial Pyramid Pooling and using the TS-YOLO structure to retain the original spatial pyramid advantage, we improve the accuracy of floating litter for detecting objects on rivers. In the experimental results, our proposed method was tested on Pascal VOC, FLOW, and WIDER FACE, which showed good detection capability on mAP with 2.86%, 1%, and 2.28% improvement over the original YOLOv4.