A Convolutional Neural Network for Real-Time Vehicle Detection Under the Unmanned Aerial Vehicle Platform

2019 ◽  
Author(s):  
Shikun Chen ◽  
Xiaobo Lu ◽  
Yongbin Li ◽  
Renliang Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Aerial Vehicle ◽  
Vehicle Platform

scholarly journals Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 71
Author(s):  
MUHAMMAD EFAN ABDULFATTAH ◽  
LEDYA NOVAMIZANTI ◽  
SYAMSUL RIZAL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Super Resolution ◽  
Image Features ◽  
Scale Factor ◽  
Aerial Imagery ◽  
Aerial Vehicle

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution

scholarly journals Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3958
Author(s):  
Seongkyun Han ◽  
Jisang Yoo ◽  
Soonchul Kwon
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Detection Method ◽  
Vehicle Detection ◽  
Research Area ◽  
Angular Distortion ◽  
Background Information ◽  
Feature Maps ◽  
Detection Model ◽  
Aerial Vehicle

Vehicle detection is an important research area that provides background information for the diversity of unmanned-aerial-vehicle (UAV) applications. In this paper, we propose a vehicle-detection method using a convolutional-neural-network (CNN)-based object detector. We design our method, DRFBNet300, with a Deeper Receptive Field Block (DRFB) module that enhances the expressiveness of feature maps to detect small objects in the UAV imagery. We also propose the UAV-cars dataset that includes the composition and angular distortion of vehicles in UAV imagery to train our DRFBNet300. Lastly, we propose a Split Image Processing (SIP) method to improve the accuracy of the detection model. Our DRFBNet300 achieves 21 mAP with 45 FPS in the MS COCO metric, which is the highest score compared to other lightweight single-stage methods running in real time. In addition, DRFBNet300, trained on the UAV-cars dataset, obtains the highest AP score at altitudes of 20–50 m. The gap of accuracy improvement by applying the SIP method became larger when the altitude increases. The DRFBNet300 trained on the UAV-cars dataset with SIP method operates at 33 FPS, enabling real-time vehicle detection.

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