An improved YOLOv5 method for large objects detection with multi-scale feature cross-layer fusion network
作者：
Highlights：
• We proposed Multi-scale Feature Cross-layer Fusion Network (M-FCFN).
• Two completely different feature scales are added as the output.
• We propose an EIOU k-means Autoanchor calculation.
• The problem of missed and false detections for large objects is improved.
• Our method on the large-scale mAP@[0.5:0.95] is 5.4% higher than YOLOv5_S.
摘要
•We proposed Multi-scale Feature Cross-layer Fusion Network (M-FCFN).•Two completely different feature scales are added as the output.•We propose an EIOU k-means Autoanchor calculation.•The problem of missed and false detections for large objects is improved.•Our method on the large-scale mAP@[0.5:0.95] is 5.4% higher than YOLOv5_S.
论文关键词：Object detection,Feature extraction,Feature fusion,K-means,Autoanchor mechanism
论文评审过程：Received 13 April 2022, Revised 7 June 2022, Accepted 5 July 2022, Available online 8 July 2022, Version of Record 20 July 2022.
论文官网地址：https://doi.org/10.1016/j.imavis.2022.104518
