Abstract
Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV, an improved version of YOLOv4-Tiny, as the solution. We benchmarked our proposed models with various state-of-the-art models on the VAID and COWC datasets. Our proposed model can achieve higher mean average precision (mAP) and frames per second (FPS) than other state-of-the-art tiny YOLO models, especially on a low-cost edge device such as the Jetson Nano 2 GB. It was observed that the Jetson Nano 2 GB can achieve up to 12.8 FPS with a model size of only 5.5 MB.
Highlights
Unmanned aerial vehicles (UAVs), commonly known as drones, are widely available commercially at a low cost
Encouraged by the previously stated problems, in this work, we propose an object detection method based on you only look once (YOLO), focusing on small object detection that could achieve near-real-time detection on low-cost hardware, coined YOLO-RTUAV
YOLOv4-Tiny, which is CSPDarknet-19; Usage of several 1 × 1 convolutional layers to reduce the complexity of the models; distance intersection over union (IoU) loss (DIoU)-nonmaximum suppression (NMS) is used to reduce suppression error and lower the occurrence of missed detection; Complete IoU loss is used to accelerate the training of the model and improve the detection accuracy; Mosaic data augmentation is used to reduce overfitting and allows the model to identify smaller-scale objects better
Summary
Unmanned aerial vehicles (UAVs), commonly known as drones, are widely available commercially at a low cost. Other traditional methods include the histogram of oriented gradients, frame difference, and optical flow These techniques have competitive inference speed due to their comparatively simple computation, but usually have low accuracy as they are trained on selected features. Many methods and algorithms are proposed to solve the issue of detecting small objects in aerial images [20,21,22]. Encouraged by the previously stated problems, in this work, we propose an object detection method based on YOLO, focusing on small object detection that could achieve near-real-time detection on low-cost hardware, coined YOLO-RTUAV. We modify the existing YOLOv4Tiny, a one-stage object detector with relatively high accuracy and speed in detecting small objects in aerial images.
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