Real-time object detection for UAVs images based on improved YOLOv5

Jiao Liu; Xiaoxia Dai

doi:10.1117/12.2684359

1 August 2023 Real-time object detection for UAVs images based on improved YOLOv5

Jiao Liu, Xiaoxia Dai

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275408 (2023) https://doi.org/10.1117/12.2684359
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Object detection is the key task for many Unmanned Aerial Vehicles (UAVs) tasks. Compared with images captured from ground-based angles, images captured by UAVs are characterized by large differences in object scales, complex backgrounds, and dense objects. Common object detection models on UAVs platforms are not very accurate or effective. To solve the above problems, in our work, we propose a real-time object detector based on improved YOLOv5N for UAVs remote sensing images. Firstly, we introduce GhostNet to replace the standard convolutional layer in the feature extraction and feature fusion network of the original YOLOv5N, thus reducing the computational overhead of the detector. Secondly, the last layer of the backbone network is substituted by a Separable Visual Transformer (SepViT) block to enhance the connectivity of the backbone with global information. Finally, the Efficient Channel- Coordinate Attention (ECCA) module, realized by combining the Efficient Channel Attention (ECA) network and the Coordinate Attention (CA) module, has also been added to YOLOv5N, by combining channel information and spatial information to highlight information that helps detect small objects. The evaluation results based on the VisDrone2019-DET dataset show that the dataset is widely used for UAV remote sensing image evaluation, and the proposed model achieves 32.9% average accuracy (mAP50), which is 6.8% better than YOLOv5N, 15.6% reduction in model computation, 6.7% reduction in inference time, and both detection accuracy and detection speed, it is suitable for deployment on UAVs for real-time object detection tasks.

Citation Download Citation

Jiao Liu and Xiaoxia Dai "Real-time object detection for UAVs images based on improved YOLOv5", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275408 (1 August 2023); https://doi.org/10.1117/12.2684359

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KEYWORDS

Object detection

Unmanned aerial vehicles

Convolution

Detection and tracking algorithms

Remote sensing

Feature fusion

Feature extraction

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