OPTIMIZATION OF THE REAL-TIME STATE-OF-THE-ART YOLOV4 OBJECT DETECTOR BY MODIFIED NECK STRUCTURE

Abstract

The state-of-the-art YOLOv4 object detector has already demonstrated its effective inference (65 frames per second (FPS) on V100 Tesla) and relatively high accuracy on MSCOCO dataset (mAP 43.5 %) in real-time mode. Moreover, simplicity of the model’s training and testing appears as another advantage for machine learning community. The ability of the model to be learned as a unified system on just a single graphic processing unit (GPU) unsurprisingly established itself as the milestone in the real-time object detection field. This work aims to review the fundamental and most recent academic work in the field and suggest the incremental research towards the optimization of the YOLOv4 architecture. We propose a model, named SAMD-YOLOv4, with modified neck structure, which reduces number of learning parameters by decreased number of filters with 1×1 kernel, which is followed by spatial attention module and dilated convolutional layers. We demonstrate that method is capable to reduce model’s complexity by 7.3% with no effect on model’s precision as well as lowered inference time by 6.9%. In Chapters below, we provide experimental results and comparison study on baseline YOLOv4 and our SAMD-YOLOv4. Furthermore, the TensorRT-based inference’s results will be revealed and studied.