Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the developments of object detection and tracking algorithms, we have organized three challenge workshops in conjunction with ECCV 2018, ICCV 2019 and ECCV 2020, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first present a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms for the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VisDrone/VisDrone-Dataset</uri> . 

/pdf/detection-and-tracking-meet-drones-challenge-1zulpzpi.pdf

Detection and Tracking Meet Drones Challenge

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors. Based on such approach, we present SlimYOLOv3 with fewer trainable parameters and floating point operations (FLOPs) in comparison of original YOLOv3 (Joseph Redmon et al., 2018) as a promising solution for real-time object detection on UAVs. We evaluate SlimYOLOv3 on VisDrone2018-Det benchmark dataset; compelling results are achieved by SlimYOLOv3 in comparison of unpruned counterpart, including ~90.8% decrease of FLOPs, ~92.0% decline of parameter size, running ~2 times faster and comparable detection accuracy as YOLOv3. Experimental results with different pruning ratios consistently verify that proposed SlimYOLOv3 with narrower structure are more efficient, faster and better than YOLOv3, and thus are more suitable for real-time object detection on UAVs. Our codes are made publicly available at this https URL

SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Tiny object detection (TOD) in aerial images is challenging since a tiny object only contains a few pixels. State-of-the-art object detectors do not provide satisfactory results on tiny objects due to the lack of supervision from discriminative features. Our key observation is that the Intersection over Union (IoU) metric and its extensions are very sensitive to the location deviation of the tiny objects, which drastically deteriorates the quality of label assignment when used in anchor-based detectors. To tackle this problem, we propose a new evaluation metric dubbed Normalized Wasserstein Distance (NWD) and a new RanKing-based Assigning (RKA) strategy for tiny object detection. The proposed NWD-RKA strategy can be easily embedded into all kinds of anchor-based detectors to replace the standard IoU threshold-based one, significantly improving label assignment and providing sufficient supervision information for network training. Tested on four datasets, NWD-RKA can consistently improve tiny object detection performance by a large margin. Besides, observing prominent noisy labels in the Tiny Object Detection in Aerial Images (AI-TOD) dataset, we are motivated to meticulously relabel it and release AI-TOD-v2 and its corresponding benchmark. In AI-TOD-v2, the missing annotation and location error problems are considerably mitigated, facilitating more reliable training and validation processes. Embedding NWD-RKA into DetectoRS, the detection performance achieves 4.3 AP points improvement over state-of-the-art competitors on AI-TOD-v2. Datasets, codes, and more visualizations are available at: https://chasel-tsui.github.io/AI-TOD-v2/ 

Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

YOLOv4_Drone: UAV image target detection based on an improved YOLOv4 algorithm

With the development of Unmanned Aerial Vehicles, drones are being deployed in a number of commercial and civil government applications ranging from remote surveillance and infrastructure maintenance among others. However, processing the videos captured by drones for the extracting meaningful information is hindered by multitude of challenges that include, the appearance of small objects, changes in viewpoint of these objects, illumination changes, large-scale resolution of the captured video, occlusion and truncation. Addressing these challenges, there is a critical need to develop algorithms that is able to efficiently process the videos that can result in robust detection and recognition of small objects. In this paper, we propose a novel processing pipeline, that brings together several key contributions including (i) the introduction of DeForm convolution layers within backbone; (ii) use of the interleaved cascade architecture; (iii) data augmentation process based on crop functionality and (iv) multi-model fusion of sub-category detection networks. The proposed approach has been exhaustively benchmarked against VisDrone-DET object detection dataset, which includes 10,209 images for training, validation and testing. The evaluation of the proposed approach has resulted in 22.61 average precision on the test-challenge set in VisDrone-DET 2019.

/pdf/dense-and-small-object-detection-in-uav-vision-based-on-24vqvrz2in.pdf

Dense and Small Object Detection in UAV Vision Based on Cascade Network

The Vision Meets Drone Object Detection in Image Challenge (VisDrone-DET 2020) is the third annual object detector benchmarking activity. Compared with the previous VisDrone-DET 2018 and VisDrone-DET 2019 challenges, many submitted object detectors exceed the recent state-of-the-art detectors. Based on the selected 29 robust detection methods, we discuss the experimental results comprehensively, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection. The full challenge results are publicly available at the website http://aiskyeye.com/leaderboard/.

VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results.

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VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results.