ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery

On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to enhance the speed-wise performance, we construct our method primarily using channel shuffling and grouped convolutions. We apply inception modules and deformable modules to consider the size and geometric shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and compared against the state-of-the-art real-time object detection networks. ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on test sets of both datasets. We show that our algorithm achieves real-time performance by running at the speed of 14 frames per second on NVIDIA Jetson TX2 showing high potential for this method for real-time processing in UAVs.Comment: Accepted in ECCV 2018, UAVision 201

SISTEM INFORMASI PARKIR MENGGUNAKAN TEKNIK OBJECT TRACKING DAN OBJECT COUNTING

Fasilitas parkir merupakan hal yang sangat penting untuk para pengguna kendaraan, khususnya kendaraan beroda empat. Di sebagian besar negara, mobil adalah moda transportasi yang dominan, bahkan diperkirakan pada tahun 2050 mendatang benua Asia akan mengalami pertumbuhan penggunaan mobil pribadi sebesar 40%. Dalam memaksimalkan efisiensi penggunaan lahan parkir, diperlukan sistem informasi parkir yang memberikan informasi ketersediaan tempat parkir untuk mempermudah pengendara menggunakan fasilitas parkir. Namun demikian, permasalahan terkait hunian parkir masih sering terjadi, seperti dari tidak adanya informasi lahan parkir yang tersedia, lambatnya informasi ketersediaan lahan parkir dan minimnya informasi terkait hunian parkir. Ditambah lagi dengan peningkatan kepemilikan mobil, menyebabkan kurangnya area parkir mobil karena tidak seimbangnya antara ketersediaan parkir dan kebutuhan parkir. Berbagai sistem berbasis sensor maupun vision dirancang untuk mengatasi masalah tersebut, namun masih terdapat kekurangan dari kinerja sistem dalam hal akurasi dan kecepatan pemrosesan. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan sistem menggunakan model YOLOv8 dengan teknik object tracking serta object counting. Teknik object tracking digunakan untuk meningkatkan akurasi dan kestabilan deteksi dalam setiap frame khususnya dalam video. Kemudian object counting dimanfaatkan dengan membuat zona deteksi untuk meningkatkan keakuratan penghitungan ketersediaan lahan parkir. Informasi yang dihasilkan dari proses deteksi, tracking, dan counting disimpan pada database lokal dan ditampilkan pada aplikasi berbasis web sehingga membantu mengetahui informasi ketersediaan parkir. Pada penelitian ini didapatkan hasil bahwa sistem yang dirancang memiliki kinerja baik dan dapat menandingi sistem yang dibangun oleh peneliti lainnya dengan akurasi rata-rata sebesar 98,67%, latensi sekitar 20 milidetik, dan 50 frame per second. ; Parking facilities are very important for vehicle users, especially four-wheeled vehicles. In most countries, cars are the dominant mode of transportation, it is estimated that by 2050 the Asian continent will experience a growth in private car use of 40%. In maximizing the efficiency of the use of parking space, a parking information system is needed, prodividng information about the availability of parking spaces to make it easier for drivers to use parking facilities. However, problems related to parking occupancy still occur frequently, such as the unavailability of parking space information, the speed of parking space information retrieved, and the lack of information about parking occupancy. Moreover, the increase in car ownership causes a shortage of car parking areas due to an imbalance between parking availability and parking needs. Various sensor and vision-based systems are designed to overcome these problems, but there are still deficiencies in system performance in terms of accuracy and processing speed. To overcome these problems, this study proposes a system using the YOLOv8 model with object tracking and object counting techniques. Object tracking techniques are used to improve the accuracy and stability of detection in each frame, especially in video. Then object counting is utilized by creating detection zones to improve the accuracy of calculating parking space availability. The information generated from the detection, tracking and counting processes is stored in a local database and displayed on a web-based application that helps determine parking availability information. In this study, the results showed that the designed system had good performance and could compete with systems built by other researchers with an average accuracy of 98.67%, a latency of around 20 milliseconds, and 50 frames per second

Counting and Locating High-Density Objects Using Convolutional Neural Network

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R$^2$) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R$^2$ of 0.975 and 0.999, respectively. The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.Comment: 15 pages, 10 figures, 8 table

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR)? The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesn't require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging