Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN)

Abstract

Tiny Object Detection (TOD) is a fundamental and difficult task in computer vision. Current state-of-the-art detectors like RCNN, Fast RCNN, Faster RCNN, SSD, and YOLO can't find small objects using single-stage or multi-stage methods. With the exponential growth of deep learning, several researchers have drawn attention to advances in tiny object detection approaches. This study proposes a TOD-CMLNN (Tiny Object Detection Competitive Multi-Layer Neural Network) architecture with three sub components first competitive multi-layer network, second TOD auxiliary and third multi-level continue features aggregation for accurately detecting small objects. Competitive learning for object detection is the basis of the proposed architecture. Comparison study with existing RCNN, Fast RCNN, Faster RCNN, SSD and YOLO shows significant improvement in the results. TOD-CMLNN receives 72.46 % accuracy in terms of mAP which is impressive as compared to state-of-the-art detectors.