Image‐based road crack risk‐informed assessment using a convolutional neural network and an unmanned aerial vehicle

2021 ◽  
Author(s):  
Ankang Ji ◽  
Xiaolong Xue ◽  
Yuna Wang ◽  
Xiaowei Luo ◽  
Luqi Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle

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 Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network

2020 ◽  
Vol 12 (10) ◽  
pp. 1668 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Yiming Liu ◽  
Cong Ou ◽  
Dehai Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Feature Fusion ◽  
Agricultural Landscapes ◽  
Sequential Dependency ◽  
Temporal Features ◽  
Multi Temporal ◽  
Aerial Vehicle

Vegetable mapping from remote sensing imagery is important for precision agricultural activities such as automated pesticide spraying. Multi-temporal unmanned aerial vehicle (UAV) data has the merits of both very high spatial resolution and useful phenological information, which shows great potential for accurate vegetable classification, especially under complex and fragmented agricultural landscapes. In this study, an attention-based recurrent convolutional neural network (ARCNN) has been proposed for accurate vegetable mapping from multi-temporal UAV red-green-blue (RGB) imagery. The proposed model firstly utilizes a multi-scale deformable CNN to learn and extract rich spatial features from UAV data. Afterwards, the extracted features are fed into an attention-based recurrent neural network (RNN), from which the sequential dependency between multi-temporal features could be established. Finally, the aggregated spatial-temporal features are used to predict the vegetable category. Experimental results show that the proposed ARCNN yields a high performance with an overall accuracy of 92.80%. When compared with mono-temporal classification, the incorporation of multi-temporal UAV imagery could significantly boost the accuracy by 24.49% on average, which justifies the hypothesis that the low spectral resolution of RGB imagery could be compensated by the inclusion of multi-temporal observations. In addition, the attention-based RNN in this study outperforms other feature fusion methods such as feature-stacking. The deformable convolution operation also yields higher classification accuracy than that of a standard convolution unit. Results demonstrate that the ARCNN could provide an effective way for extracting and aggregating discriminative spatial-temporal features for vegetable mapping from multi-temporal UAV RGB imagery.

scholarly journals IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI TANAMAN PADA CITRA RESOLUSI TINGGI

GEOMATIKA ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. 61 ◽  
Author(s):  
Erlyna Nour Arrofiqoh ◽  
Harintaka Harintaka
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle

Citra resolusi tinggi dari teknologi UAV (Unmanned Aerial Vehicle) dapat memberikan hasil yang baik dalam ekstraksi informasi sehingga dapat digunakan untuk monitoring dan updating data suatu wilayah. Pengambilan informasi dari citra dengan interpretasi visual sangat bergantung pada interpreter. Kendala utama interpretasi secara manual adalah saat melakukan pengenalan objek secara visual, khususnya pada objek tanaman pertanian. Kesalahan hasil asumsi interpreter dapat terjadi ketika citra yang diekstraksi memiliki objek yang kompleks dan memiliki karakter fisik yang hampir mirip apabila dilihat dari foto udara yang hanya memiliki band RGB (Red, Green, dan Blue). Penelitian ini mencoba mengimplementasikan pendekatan klasifikasi semantik secara otomatis yang dapat membedakan jenis tanaman sebagai alternatif pengenalan objek berdasarkan metode deep learning menggunakan Convolutional Neural Network (CNN). Metode CNN merupakan salah satu metode deep learning yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek dan klasifikasi serta dapat diterapkan pada citra resolusi tinggi yang memiliki model distribusi nonparametrik. Pada penelitian ini, diterapkan algoritma CNN untuk membedakan jenis tanaman dengan memberikan label semantik dari objek jenis tanaman. Penelitian menggunakan 5 kelas jenis tanaman, yaitu kelas tanaman padi, bawang merah, kelapa, pisang, dan cabai. Proses learning jaringan menghasilkan akurasi 100% terhadap data training. Pengujian terhadap data validasi menghasilkan akurasi 93% dan akurasi terhadap data tes 82%. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN berpotensi untuk pendekatan pengenalan objek secara otomatis dalam membedakan jenis tanaman sebagai bahan pertimbangan interpreter dalam menentukan objek pada citra.

Sign in / Sign up

Export Citation Format

Share Document