Field Dynamic Small Object Detection Network based on Double Frame Fusion

doi:10.7523/j.ucas.2023.008

Abstract

Abstract: Detecting dynamic small objects in complex environments in the field remains a challenging problem for defense military applications due to factors such as more background interference in the field surveillance sensing systems, fewer pixels of small targets, and the lack of relevant open datasets. In order to solve this problem, a YOLOv5-based object detection network with double frame feature fusion (YOLO-DFNet) is proposed. Firstly, a double frame feature fusion module(D-F fusion) is proposed to process the adjacent frame features from the backbone network, calculating attention in channel and time as well as space successively, to extract motion features; secondly, a temporal trapezoidal fusion network based on attention mechanism(TTFN_AM) is designed between the neck network and the detection head to focus on dynamic objects on receptive fields of different sizes to improve the detection effect of small objects of large displacement. The experimental results on field motion small object dataset (FMSOD) show that the mean average precision (mAP) on different IoUs of the YOLO-DFNet proposed in this paper is 3.9% higher than the YOLOv5, and also outperforms other object detection models such as Tph-YOLOv5 and YOLOv7.

Key words: object detection, field monitoring sensor network, dynamic small object, double-frame feature fusion, spatial-temporal attention

CLC Number:

TP391.4

ZHAO Xiaohan, ZHANG Zebin, LI Baoqing. Field Dynamic Small Object Detection Network based on Double Frame Fusion[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2023.008.

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