Version 1
: Received: 13 January 2024 / Approved: 15 January 2024 / Online: 15 January 2024 (08:08:24 CET)
How to cite:
Lu, Y.; Sun, M. SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy. Preprints2024, 2024011108. https://doi.org/10.20944/preprints202401.1108.v1
Lu, Y.; Sun, M. SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy. Preprints 2024, 2024011108. https://doi.org/10.20944/preprints202401.1108.v1
Lu, Y.; Sun, M. SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy. Preprints2024, 2024011108. https://doi.org/10.20944/preprints202401.1108.v1
APA Style
Lu, Y., & Sun, M. (2024). SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy. Preprints. https://doi.org/10.20944/preprints202401.1108.v1
Chicago/Turabian Style
Lu, Y. and Minghao Sun. 2024 "SSE-YOLO: Efficient UAV Target Detection With Less Parameters and High Accuracy" Preprints. https://doi.org/10.20944/preprints202401.1108.v1
Abstract
Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.
Keywords
UAV; small target detection; YOLOv8; feature extraction; deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
