Improving the YOLOv5 Deep Neural Network for Detecting Vehicles and Outdoor Pools from Drone Data

Detecting small objects such as vehicles and swimming pools in high-spatial-resolution drone images is challenging due to their similar geometric and color features. The increase in the number of vehicles is not only a major challenge from the perspective of urban traffic but also leads to environmental problems such as pollution and warming. Therefore, monitoring these targets can play an important role in managing these problems. On the other hand, the construction and maintenance of swimming pools also require a significant amount of water, and monitoring these targets in urban environments is essential for water conservation. In this regard, drone remote sensing images and deep learning networks, which have a high ability to detect objects from these images, are considered suitable tools for monitoring these targets. Although valuable research has been done in this area to address each of the environmental challenges mentioned, there are still shortcomings in them. In this study, a new deep learning network YOLOv5+ has been developed to simultaneously detect two targets, vehicles and swimming pools, from drone images, in which the network's performance in extracting efficient features has been enhanced due to the use of the Inception mechanism in the intermediate layers. Additionally, in this study, DJI Mavic and DJI Mini Se drone data from Tianjin regions in China and the city of Cannes in France were used to evaluate the performance of the proposed network and compare it with the YOLOv5 and YOLOv7 deep learning networks. Finally, the results showed that the proposed network achieved an overall accuracy of 95% on the test set, which is an improvement of 2% over the YOLOv5 and YOLOv7 networks, indicating the efficiency of the approach proposed in this study.