Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Abstract

Abstract-Unmanned Aerial Vehicle (UAV) technology is advancing at a fast pace following the strong rise in interest in its applications for a wide variety of scenarios. One of the promising use cases for UAVs is their deployment during emergency and rescue operations. Their high mobility, aerial viewpoint and flexibility to be operated autonomously are huge assets during crises. UAVs exist in a wide range when it comes to cost, and so do the sensors or accessories they can carry along as payload. During emergencies it may be beneficial to have several low-cost UAVs on site, as opposed to one highly sophisticated, fully-equipped aerial platform. This way more ground can be covered in a shorter span of time during search-and-rescue operations. The situation where a single UAV is on the ground to recharge its battery can also be avoided. In this paper, we present an architecture that identifies and locates objects of interest in real-time using low-cost hardware and a state of the art object detection algorithm. We avoid the use of expensive LIDAR sensors and UAVs, but still manage to determine the position of specific predefined objects in the field with high precision. Without augmenting our detection setup by using intelligent adaptive flight patterns, we can locate an object within 1.5m of its actual location at very low latency. Our detection chain delivers the result and its location in well under one second.

DeepHAZMAT: Hazardous Materials Sign Detection and Segmentation with Restricted Computational Resources

“…The trained model is also able to detect occluded, overlapped, and partially visible signs. The experimental results showed that the DeepHAZMAT model is more accurate and faster than many other recent and state-or-the-art research works such as [18], [19], and [20]. The developed DNN-based system was fast enough to be implemented in Mobile robots, using a single Intel NUC Corei7 embedded system for robust and real-time hazard label detection, recognition, identification, localisation, and segmentation, thanks to skipping redundant input frames as well as adaptation of the YOLOv3-tiny for our real-time robotics application.…”

“…Nils et al [19], train a deep neural network model based on the YOLOv2 algorithm for hazmat sign detection. Although their model performs fast on a GPU platform, their model is not real-time on CPUs and the system has an error of up to 1.5" in localising the hazmat signs.…”

Embedded Real-Time Object Detection for a UAV Warning System

“…Preferably we want a solution that doesn't require a complex on-board setup (LiDAR or other expensive and/or heavy imaging system) to minimize cost and maximize flight time. Tijtgat et al [25] present a solution that matches this requirement and provides a positional accuracy of <1.5m. The work presented in [13] proposes a vision-based UAV approach and landing scenario.…”

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

“…The Cascaded-CNN (C-CNN) model has been implemented with the TX2 and applied to the classification of weeds in multi-spectral images in intelligent agriculture. These studies [23][24][24][25] [26] have shown that the embedded hardware of the Jetson series is effective in target detection and has the advantages of having high efficiency and low power consumption.…”