Version 1
: Received: 16 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (12:57:33 CEST)
How to cite:
Sun, C.; Zhang, Y.; Ma, S. DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery. Preprints2024, 2024071302. https://doi.org/10.20944/preprints202407.1302.v1
Sun, C.; Zhang, Y.; Ma, S. DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery. Preprints 2024, 2024071302. https://doi.org/10.20944/preprints202407.1302.v1
Sun, C.; Zhang, Y.; Ma, S. DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery. Preprints2024, 2024071302. https://doi.org/10.20944/preprints202407.1302.v1
APA Style
Sun, C., Zhang, Y., & Ma, S. (2024). DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery. Preprints. https://doi.org/10.20944/preprints202407.1302.v1
Chicago/Turabian Style
Sun, C., Yihong Zhang and Shuai Ma. 2024 "DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery" Preprints. https://doi.org/10.20944/preprints202407.1302.v1
Abstract
Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhance dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FC-C2f, which reduces the model's parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model's classification ability and detection accuracy. Experimental results on the SeaDroneSea dataset indicate that DFLM-YOLO improves mAP by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for UAVs to conduct object detection missions in open water efficiently.
Keywords
lightweight; multiscale feature fusion; data augmentation; UAV; object detection
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.
