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Open Access

ARTICLE

Lightweight Airborne Vision Abnormal Behavior Detection Algorithm Based on Dual-Path Feature Optimization

Baixuan Han1, Yueping Peng1,*, Zecong Ye2, Hexiang Hao1, Xuekai Zhang1, Wei Tang1, Wenchao Kang1, Qilong Li1
1 School of Information Engineering, Engineering University of PAP, Xi’an, 710086, China
2 Unit Command Department, Officers College of PAP, Chengdu, 610213, China
* Corresponding Author: Yueping Peng. Email: email
(This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071071

Received 30 July 2025; Accepted 12 September 2025; Published online 08 October 2025

Abstract

Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm, a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed. By integrating multi-head grouped self-attention mechanism and Partial-Conv, a two-way feature grouping fusion module (DFPF) was designed, which carried out effective channel segmentation and fusion strategies to reduce redundant calculations and memory access. C3K2 module was improved, and then unstructured pruning and feature distillation technology were used. The algorithm model is lightweight, and the feature extraction ability for airborne visual abnormal behavior targets is strengthened, and the computational efficiency of the model is improved. Finally, we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset. The results show that com-pared with the baseline model, the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%, and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision. The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model, indicating the effectiveness of the improved method in this paper.

Keywords

YOLOv11 algorithm; multi-class abnormal behavior detection; feature extraction; UAV aerial photography datasets

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