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/. The results demonstrate that there still remains a large room for improvement for object detection algorithms on drones.
Almost all successful nodule detectors rely heavily on a fixed set of anchor boxes. In this paper, inspired by the success of the keypoint estimation method in natural image detection, we propose an anchor-free framework for accurate pulmonary nodule detection. We first present a novel representation for detecting nodules, in terms of their 3D center locations, which reduces the number of hyper-parameters and the corresponding computation related to anchors, thus making the nodule detection pipeline much simpler. Then, an effective two-stream network is introduced to reduce the false positive nodule candidates, by aggregating information from the image stream and motion-history stream. Experiments show that the proposed approach achieves a sensitivity of 96.1%, with 8 false positives per scan, and a CPM score of 90.6%, on the publicly available LUNA16 dataset, which outperforms other state-of-the-art methods. By testing on the SPIE-AAPM dataset with models pre-trained on the LUNA16, our proposed method yields 92.8% sensitivity with 8 false positives per scan. This demonstrates the effectiveness and generalization ability of our method.
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