HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

Abstract

We present the HIT-UAV dataset, a high-altitude infrared thermal dataset for object detection applications on Unmanned Aerial Vehicles (UAVs). The dataset comprises 2,898 infrared thermal images extracted from 43,470 frames in hundreds of videos captured by UAVs in various scenarios, such as schools, parking lots, roads, and playgrounds. Moreover, the HIT-UAV provides essential flight data for each image, including flight altitude, camera perspective, date, and daylight intensity. For each image, we have manually annotated object instances with bounding boxes of two types (oriented and standard) to tackle the challenge of significant overlap of object instances in aerial images. To the best of our knowledge, the HIT-UAV is the first publicly available high-altitude UAV-based infrared thermal dataset for detecting persons and vehicles. We have trained and evaluated well-established object detection algorithms on the HIT-UAV. Our results demonstrate that the detection algorithms perform exceptionally well on the HIT-UAV compared to visual light datasets, since infrared thermal images do not contain significant irrelevant information about objects. We believe that the HIT-UAV will contribute to various UAV-based applications and researches. The dataset is freely available at https://pegasus.ac.cn.

An Efficient Rep-Style Gaussian–Wasserstein Network: Improved UAV Infrared Small Object Detection for Urban Road Surveillance and Safety

“…The HIT-UAV dataset is a collection of UAV infrared remote sensing images provided by Zhang et al [25]. A drone DJI Matrice M210 V2 is used to obtain the dataset, and a DJI Zenmuse XT2 infrared camera is mounted on the drone, which has a resolution of 640 × 512 pixels and a 25 mm lens.…”

“…The ERGW-net leverages state-of-the-art networks (i.e., Resnet and InceptionNet), adds an improved loss function, and lays the foundation for a vast array of improvements. Using experiments, we verify its improved effectiveness on a DroneVehicle dataset [24] and the famous HIT-UAV dataset [25]. The main contributions of our method can be summarized as follows:…”

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

“…Suo et al [10] presents the HIT-UAV dataset, which is a high-altitude infrared thermal dataset designed for object detection on Unmanned Aerial Vehicles (UAVs). The dataset contains 2,898 infrared thermal images extracted from videos captured by UAVs in various scenarios.…”

“…Table 1 shows the parameter value of YOLO model. This research employs a larger number of epochs compared to previous study [10] because involving more epochs provides the model with more opportunities to adapt to patterns and features present in the training data. This can enhance the model's ability to comprehend more abstract and complex representations.…”

“…In the previous study [10], several models such as YOLOv4, Faster-RCNN, and SSD-512 were employed on the HIT-UAV dataset. In that research, SSD-512 appeared to be more accurate than other models.…”