BiFPN-YOLO: One-stage object detection integrating Bi-Directional Feature Pyramid Networks

Abstract

Object detection is a key component in computer vision research, allowing a system to determine the location and type of object within any given scene. YOLOv5 is a modern object detection model, which utilises the advantages of the original YOLO implementation while being built from scratch in Python. In this paper, BiFPN-YOLO is proposed, featuring clear improvements over the existing range of YOLOv5 object detection models; these include replacing the traditional Path-Aggregation Network (PANet) with a higher performing Bi-Directional Feature Pyramid Network (BiFPN), requiring complex adaptation from its original implementation to function with YOLOv5, as well as exploring a replacement to the standard Swish activation function by evaluating the performance against a number of other activation functions. The proposed model showcases state-of-the-art performance, benchmarking against well-known datasets such as the German Traffic Sign Detection Benchmark (GTSDB), improving mAP by 3.1 %, and the RoboFEI@Home dataset, where Mean Average Precision (mAP) is improved by 2 % compared to the base YOLOv5 model. Performance was also improved on MSCOCO by 1.1 % and a custom subset of the OpenImagesV6 dataset by 2.4 %.