Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Abstract

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

“…It can provide real-time, centimeter-level positioning data for improved absolute accuracy on image metadata. The use of real-time detection of young oil palm biophysical parameters using UAV is advantageous because at the young stage, there is a rapid growth of biophysical parameters and it can be helpful to monitor the health of oil palm in case of pest infestation [13].…”

“…Real-time quantification of these parameters can be useful for detecting the health of a tree, which allows the selection of appropriate remedial measures such as the use of fertilizer, insecticides, and irrigation to improve tree health. Meng et al studied real-time detection of ground objects using unmanned aerial vehicle (UAV) and deep learning methods in China [13].…”

A Deep Learning Approach Towards Railway Safety Risk Assessment

“…For less operational constraints, two smartphones have been used as stereo cameras to acquire motion data and extract 3D human skeletons to track people working in construction fields [98]. Real-time machine learning models with CNN frameworks have been proposed to detect whether workers are wearing safety equipment, such as hats and vests, from images/videos [99] and to detect ground objects [100]. CNNs have also been used to detect safety guardrails [101], objects on roof construction sites [102], workers who fail to wear hard hats [103], [104], falls from heights [105], [106], to maintain safe distances among objects for safety to prevent accidents [107] and unsafe behaviours [73].…”

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

“…Unmanned aerial vehicle applications and new methods in photogrammetry [1] and remote sensing have increased rapidly in recent years [2][3][4][5]. Currently, unmanned aerial vehicles (UAVs) are used by a wide community and for cases and applications that could not be performed in the past.…”