2019 · 514 citations · 40 references
EngineeringAerospace EngineeringComputer VisionMachine VisionUnmanned Aircraft SystemsObject DetectionObject RecognitionRobotic VisionAutomated Object Detection
Automatic visual data understanding from drone platforms is increasingly demanded. The challenge aims to advance state‑of‑the‑art drone object detection and provide a comprehensive evaluation platform. It employs a publicly available evaluation protocol and dataset, with results posted online. Results from 33 detectors show significant room for improvement in drone object detection. Results and resources are available at http://www.aiskyeye.com/.
Recently, automatic visual data understanding from drone platforms becomes highly demanding. To facilitate the study, the Vision Meets Drone Object Detection in Image Challenge is held the second time in conjunction with the 17-th International Conference on Computer Vision (ICCV 2019), focuses on image object detection on drones. Results of 33 object detection algorithms are presented. For each participating detector, a short description is provided in the appendix. Our goal is to advance the state-of-the-art detection algorithms and provide a comprehensive evaluation platform for them. The evaluation protocol of the VisDrone-DET2019 Challenge and the comparison results of all the submitted detectors on the released dataset are publicly available at the website: http: //www.aiskyeye.com/. http: //www.aiskyeye.com/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.
40
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · 2016 · 222.1K citations · Full text
Kaiming He, Georgia Gkioxari, Piotr Dollár et al. · 2017 · 28.8K citations
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. · 2017 · 28.7K citations
Squeeze-and-Excitation Networks