# Unmanned Airborne Target Detection Method with Multi-Branch Convolution and Attention-Improved C2F Module

## TL;DR

This paper introduces a new target detection algorithm for small objects in drone imagery using improved convolution and attention mechanisms.

## Contribution

The novel C2F module with multi-branch convolution and attention improves small target detection in aerial images.

## Key findings

- The proposed algorithm improved detection metrics by 2.8-9.2% on two datasets.
- The FA-Block module enhances feature fusion for small targets.
- Lightweight up-sampling increases the network’s sensory field.

## Abstract

In this paper, a target detection network algorithm based on a multi-branch convolution and attention improvement Cross-Stage Partial-Fusion Bottleneck with Two Convolutions (C2F) module is proposed for the difficult task of detecting small targets in unmanned aerial vehicles. A C2F module method consisting of fusing partial convolutional (PConv) layers was designed to improve the speed and efficiency of extracting features, and a method consisting of combining multi-scale feature fusion with a channel space attention mechanism was applied in the neck network. An FA-Block module was designed to improve feature fusion and attention to small targets’ features; this design increases the size of the miniscule target layer, allowing richer feature information about the small targets to be retained. Finally, the lightweight up-sampling operator Content-Aware ReAssembly of Features was used to replace the original up-sampling method to expand the network’s sensory field. Experimental tests were conducted on a self-complied mountain pedestrian dataset and the public VisDrone dataset. Compared with the base algorithm, the improved algorithm improved the mAP50, mAP50-95, P-value, and R-value by 2.8%, 3.5%, 2.3%, and 0.2%, respectively, on the Mountain Pedestrian dataset and the mAP50, mAP50-95, P-value, and R-value by 9.2%, 6.4%, 7.7%, and 7.6%, respectively, on the VisDrone dataset.