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CGF-YOLOv11n : An efficient and real-time small object detection algorithm based on YOLOV11n

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

Background: Applications for small target identification are many and include security surveillance, autonomous driving, and remote sensing. The conventional tiny item identification techniques have significant data labeling costs, poor resolution, and high computational resource consumption.

Methods: We present CGF-YOLOv11n, a real-time and effective tiny object identification model built using YOLOv11n. First, the C2PLUS module was created, successfully resolving the issue of inadequate feature extraction for tiny targets. Second, a plug-and-play convolutional module for GRFAConv was created, improving the backbone network’s feature extraction capabilities. Lastly, to encourage the harmonious integration of semantic content and geographical information, the FDPN module was created. The VisDrone2019 dataset is available at: https://github.com/VisDrone/VisDrone-Dataset

Results: According to the experimental findings, the enhanced algorithm outperformed the baseline model by 3.5% and 3.1%, respectively, on the validation and test sets of the VisDrone2019 dataset using [email protected]. Furthermore, this model’s real-time performance of 34 frames per second when implemented on the Orange PI 5 produced the impact of real-time detection and aided in the advancement of tiny target detection technology. The code and data used for this research are available at: https://github.com/trushbin/bin/tree/main

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