TY - GEN
T1 - Drone-Assisted Inspection for Automated Accident Damage Estimation
T2 - 11th International Conference on Ubiquitous and Future Networks, ICUFN 2019
AU - Serhani, M. Adel
AU - Ng, Tony T.
AU - Al Falasi, Asma
AU - Saedi, Meera Al
AU - Nuaimi, Fatima Al
AU - Shamsi, Hamda Al
AU - Shamsi, Al Damani Al
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Drones have been used in many application domains nowadays including traffic congestion control, weather information collection, disaster and rescue interventions, and surveillance operations. The drone adoption lies on their capabilities to collect images, videos as well as other sensory data from the air, stream this data to the cloud for processing, and analytics in order to derive important real-time decisions. In this paper, we propose a drone assisted inspection for accident damage estimation based on deep learning approach. Drones are automatically scheduled to visit the accident locations, and data is retrieved for further processing and analytics. We developed a two-phases damage estimation approach, where in the first phase we use deep learning approach to identify and classify objects from accident's images, and in the second phase we measure the size of damaged objects and we estimate the overall cost of the accident's damages. We evaluated our two-phase approach using data of various accidents, and the classification accuracy we have obtained vary between 0.79 and 0.94 and the accident's damage cost estimation most of time is 100% accepted by the expert.
AB - Drones have been used in many application domains nowadays including traffic congestion control, weather information collection, disaster and rescue interventions, and surveillance operations. The drone adoption lies on their capabilities to collect images, videos as well as other sensory data from the air, stream this data to the cloud for processing, and analytics in order to derive important real-time decisions. In this paper, we propose a drone assisted inspection for accident damage estimation based on deep learning approach. Drones are automatically scheduled to visit the accident locations, and data is retrieved for further processing and analytics. We developed a two-phases damage estimation approach, where in the first phase we use deep learning approach to identify and classify objects from accident's images, and in the second phase we measure the size of damaged objects and we estimate the overall cost of the accident's damages. We evaluated our two-phase approach using data of various accidents, and the classification accuracy we have obtained vary between 0.79 and 0.94 and the accident's damage cost estimation most of time is 100% accepted by the expert.
KW - Damage inspection
KW - classification
KW - deep learning
KW - drone
KW - estimation
KW - object image extraction
KW - size measurement
UR - http://www.scopus.com/inward/record.url?scp=85071877635&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071877635&partnerID=8YFLogxK
U2 - 10.1109/ICUFN.2019.8806100
DO - 10.1109/ICUFN.2019.8806100
M3 - Conference contribution
AN - SCOPUS:85071877635
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 682
EP - 687
BT - ICUFN 2019 - 11th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
Y2 - 2 July 2019 through 5 July 2019
ER -