diff --git a/README.md b/README.md index 8922d5bf920a1dddbd47f0076a2294466c11302f..2d3f021684ee8c71f79820388d5a088f0e78cc29 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -PFE: Drone Position Estimation +PFE: Drone Detection & Position Estimation -------------------------------------- # Author @@ -6,10 +6,14 @@ PFE: Drone Position Estimation - selim.lakhdar.etu@univ-lille.fr - selim.lakhdar@gmail.com +# Objectif +- caméra débarquée: + - détecter la flotte de drones en temps réel et localiser chaque robot en X, Y et idéalement avec l'attitude. + # Articles -- Dist-YOLO: Fast Object Detection with Distance Estimation - - source code: https://gitlab.*c*om/EnginCZ/yolo-with-distance +- [Dist-YOLO: Fast Object Detection with Distance Estimation](doc/articles/Dist-YOLO_Fast_Object_Detection_with_Distance_Estimation.pdf) + - source code: https://gitlab.com/EnginCZ/yolo-with-distance - abstract: ``` We present a scheme of how YOLO can be improved in order to predict the absolute distance of objects using only information from a monocular camera. It is fully integrated into the original architecture by extending the prediction vectors, sharing the backbone’s weights with the bounding box regressor, and updating the original loss function by a part responsible for distance estimation. @@ -19,7 +23,7 @@ We show that using the KITTI dataset, the proposed scheme yields a mean relative approaches. Finally, we show that the inference speed is identical to the unmodified YOLO, 45 frames per second. ``` -- Automated Drone Detection Using YOLOv4 +- [Automated Drone Detection Using YOLOv4](doc/articles/Automated_Drone_Detection_Using_YOLOv4.pdf) - abstract: ``` Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this research, we designed an automated drone detection system using YOLOv4. The model was trained using drone and @@ -29,7 +33,7 @@ better performance than what has been found in previous similar studies, achievi on the DJI Phantom III and an FPS of 19.0 on the DJI Mavic Pro. ``` -- Learning Object-Specific Distance From a Monocular Image +- [Learning Object-Specific Distance From a Monocular Image](doc/articles/Zhu_Learning_Object-Specific_Distance_From_a_Monocular_Image_ICCV_2019_paper.pdf) - abstract: ``` Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving. Many attentions have been paid on the object detection task, but distance estimation only arouse few interests in the computer vision community. Observing that the traditional inverse perspective mapping algorithm performs poorly for objects far away from the camera or on the curved road, in this paper, we address the challenging @@ -39,9 +43,22 @@ model with a keypoint regressor, where a projection loss is defined to enforce a road. Moreover, the performance margin implies the effectiveness of our enhanced method ``` +- [YOLODrone: Improved YOLO Architecture for Object Detection in Drone Images](doc/articles/YOLODrone_Improved_YOLO_Architecture_for_Object_Detection_in_Drone_Images.pdf) + - abstract: +``` Recent advances in robotics and computer vision fields yield emerging new applications for camera equipped drones. One such application is aerial-based object detection. However, despite the recent advances in the relevant literature, +object detection remains as a challenging task in computer vision. Existing object detection algorithms demonstrate even lower performance on drone (or aerial) images since the object detection problem is a more challenging problem in aerial images, +when compared to the detection task in ground-taken images. There are many reasons for that including: (i) the lack of large drone datasets with large object variance, (ii) the larger variance in both scale and orientation in drone images, and (iii) the +difference in shape and texture features between the ground and the aerial images. In this paper, we introduce an improved YOLO algorithm: YOLODrone for detecting objects in drone images. We evaluate our algorithm on VisDrone2019 dataset and report +improved results when compared to YOLOv3 algorithm. +``` + # Links ## YOLO - https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/ +- https://github.com/chuanenlin/drone-net + - DroneNet is Joseph Redmon's YOLO real-time object detection system retrained on 2664 images of DJI drones, labeled. +- https://pjreddie.com/darknet/ +- https://github.com/dnjstjr93/Drone-Detection_YOLOv3_PyTorch ## Distance - https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/ diff --git a/doc/articles/Automated Drone Detection Using YOLOv4.pdf b/doc/articles/Automated_Drone_Detection_Using_YOLOv4.pdf similarity index 100% rename from doc/articles/Automated Drone Detection Using YOLOv4.pdf rename to doc/articles/Automated_Drone_Detection_Using_YOLOv4.pdf diff --git a/doc/articles/Dist-YOLO: Fast Object Detection with Distance Estimation.pdf b/doc/articles/Dist-YOLO_Fast_Object_Detection_with_Distance_Estimation.pdf similarity index 100% rename from doc/articles/Dist-YOLO: Fast Object Detection with Distance Estimation.pdf rename to doc/articles/Dist-YOLO_Fast_Object_Detection_with_Distance_Estimation.pdf diff --git a/doc/articles/YOLODrone_Improved_YOLO_Architecture_for_Object_Detection_in_Drone_Images.pdf b/doc/articles/YOLODrone_Improved_YOLO_Architecture_for_Object_Detection_in_Drone_Images.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e72a5f15848395840cf9c4aac456402ef237bcb1 Binary files /dev/null and b/doc/articles/YOLODrone_Improved_YOLO_Architecture_for_Object_Detection_in_Drone_Images.pdf differ