Research on a Dense Pedestrian-Detection Algorithm Based on an Improved YOLO11

Liang Wu, Xiang Li (), Ping Ma and Yicheng Cai
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Liang Wu: School of Information Media, Hubei Land Resources Vocational College, Wuhan 430090, China
Xiang Li: School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
Ping Ma: College of Natural Resources Geographic Information, Hubei Land Resources Vocational College, Wuhan 430090, China
Yicheng Cai: School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China

Future Internet, 2025, vol. 17, issue 10, 1-19

Abstract: Pedestrian detection, as a core function of an intelligent vision system, plays a key role in obstacle avoidance during driverless navigation, intelligent traffic monitoring, and other fields. In this paper, we optimize the YOLO11 detection algorithm to solve the problem of insufficient accuracy of pedestrian detection in complex scenes. The C3K2-lighter module is constructed by replacing the Bottleneck in the C3K2 module with the FasterNet Block, which significantly enhances feature extraction for long-distance pedestrians in dense scenes. In addition, it incorporates the Triplet Attention Module to establish correlations between local features and the global context, thereby effectively mitigating omission problems caused by occlusion. The Variable Focus Loss Function (VFL) is additionally introduced to optimize target classification by quantifying the variance in features between the predicted frame and the ground-truth frame. The improved model, YOLO11-Improved, achieves a synergistic optimization of detection accuracy and computational efficiency, increasing the AP value by 3.7% and the precision by 2.8% and reducing the parameter volume by 0.5 M while maintaining real-time performance.

Keywords: real-timeobject detection; dense pedestrian detection; computer vision; machine learning; occlusion handling; YOLO11-Improved (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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