Abstract
Abstract. Currently, deployment of UAV has transformed from crucial to day-to-day scenarios for various purposes such as wastage collection, live entertainment, product delivery, town mapping, etc. Object tracking based UAV applications such as traffic monitoring, wildlife monitoring and surveillance have undergone phenomenal changeover due to deep learning based methodologies. With such transformation, there is also lack of resources to practically explore the UAV images and videos with deep learning methodologies. Hence, a deep learning-based object detection and tracking tool with UAV data (DL-ODT-UAV) is proposed to fill the learning gap, especially among students. DL-ODT-UAV is a resource to acquire basic knowledge about UAV and deep learning based object detection and tracking. It integrates various object annotators, object detectors and object tracker. Single object detection and tracking is performed with YOLO as object detector and LSTM as object tracker. Faster R-CNN is adopted in multiple object detection. With exploring the tool, the ability of students to approach problems related to deep learning methodologies will improve to a greater level.
Highlights
INTRODUCTIONUnmanned Aerial Vehicle(UAV) deployment has created tremendous growth in fields such as disaster recovery (Erdelj and Natalizio, 2016), traffic surveillance (Khan et al, 2017), ecology monitoring (Madhavan et al, 2018), forest surveillance (Berie and Burud, 2018), land mapping, road mapping, town mapping (“Mapping India through drones,” 2019), research in earth science, wildlife and maritime monitoring (Hodgson et al, 2018), product delivery (Haque et al, 2014), military purposes (Cyprian Aleksander, 2018)and police investigation (Ndna and Tss, 2017)
Recurrent YOLO (ROLO) model is adopted for Deep learning (DL) based single object detection and tracking with UAV data (Ning et al, 2017)
The precision and recall obtained for single object detection and tracking module is 90.83% and 92.09% and for multiple object detection module is 91.23% and 93.51% (Table 1)
Summary
Unmanned Aerial Vehicle(UAV) deployment has created tremendous growth in fields such as disaster recovery (Erdelj and Natalizio, 2016), traffic surveillance (Khan et al, 2017), ecology monitoring (Madhavan et al, 2018), forest surveillance (Berie and Burud, 2018), land mapping, road mapping, town mapping (“Mapping India through drones,” 2019), research in earth science, wildlife and maritime monitoring (Hodgson et al, 2018), product delivery (Haque et al, 2014), military purposes (Cyprian Aleksander, 2018)and police investigation (Ndna and Tss, 2017). The transformation of feature engineering-based object detection and tracking to deep learning-based object detection and tracking enhance the accuracy of tracking based UAV applications. Traditional object detection methodologies such as SURF, SIFT (Micheal and Vani, 2018) , Harris corner operator (Yu et al, 2008) and Enhanced Viola-Jones (Xu et al, 2017) have been experimented by the researchers for object detection in UAV images. DL based object detection and tracking methodologies gain importance to adapt UAV videos over traditional methodologies. UAV has reached farmers in a remote village of India to spray pesticides in agricultural farms(“Farmers use drones to spray pesticide,” 2019). With such efforts to reach UAV to a layman, lack of materials to handle UAV data among students and researchers still persist. To provide a practical exploration of DL based object detection and tracking with UAV data
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