ShadowDeNet: A Moving Target Shadow Detection Network for Video SAR

Bao, Jinyu; Zhang, Tianwen; Xu, Xiaowo

doi:10.3390/rs14020320

Cited by 12 publications

(5 citation statements)

References 63 publications

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“…Walaupun banyak digunakan untuk deteksi objek seperti kerusakan jalan [1], transportasi [5], hewan [7], penggunaannya untuk deteksi bayangan masih terbatas. Penelitian oleh Bao dkk [6], menggunakan Faster RCNN untuk deteksi bayangan pada video Synthetic Aperture Radar (SAR), menunjukan akurasi yang lebih tinggi sebesar 9% dibandingkan dengan tingkat dasar eksperimental Faster R-CNN.…”

Section: Pendahuluanunclassified

“…Pengujian sistem deteksi dilakukan dengan menentukan nilai confusion matrix. Kami menggunakan nilai F1-score sebagai indeks akurasi karena memungkinkan untuk menemukan titik keseimbangan antara tingkat deteksi yang tinggi (recall tinggi) dan tingkat peringatan palsu yang rendah (presisi rendah) [6]. Dengan demikian, nilai F1-score dapat memberikan indikasi tentang seberapa baik sistem dapat mengenali bayangan yang benar sambil meminimalkan kesalahan dalam memberikan peringatan palsu.…”

Section: Evaluasi Menggunakan Confusion Matrixunclassified

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Deteksi Objek Bayangan Kendaraan Menggunakan Faster R-CNN

Nabila

2024

Prosiding Seminar Nasional Fisika (E-Journal) snf2016 Unj

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Objek bayangan memiliki intensitas dan bentuk yang beragam, yang dapat menimbulkan masalah dalam sistem visi kendaraan otonom. Bayangan yang ditimbulkan dari pohon, bangunan, d an objek la in di sekitar jalan dapat mempengaruhi kinerja sistem pengenalan dan pelacakan target. Maka itu, diperlukan suatu model pendeteksian bayangan untuk mengetahui lokasi ba yangan agar dapat digunakan pada penelitian terkat eliminasi bayangan. Penelitian ini bertujuan untuk mengetahui tingkat akurasi model dengan variasi dataset yang kami berikan dan mendefinisikan masing-masing label objek non-shadow dan shadow merupakan metode yang digunakan untuk membedakan antara bayangan dan objeknya yang mirip. Pelatihan model dilakukan dengan fine-tuning Faster R-CNN pada kerangka kerja Pytorch menggunakan arsitektur ResNet50 sebagai rancangan dasar. Implementasi model untuk dapat mendeteksi bayangan diterapkan pada video perjalanan kendaraan otonom . Hasil penelitian menunjukkan bahwa dari kelima model yang dibuat, model P5 berhasil mendeteksi bayangan dengan rata-rata akurasi F1-score sebesar 46%.

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Section: Pendahuluanunclassified

Section: Evaluasi Menggunakan Confusion Matrixunclassified

Deteksi Objek Bayangan Kendaraan Menggunakan Faster R-CNN

Nabila

2024

Prosiding Seminar Nasional Fisika (E-Journal) snf2016 Unj

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“…Bao et al proposed a deep learning model for shadow detection of moving ground targets on video Synthetic Aperture Radar (SAR) images [94]. ground discrimination capacity of model.…”

Section: B State-of-the-art Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

et al. 2022

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Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to Queue estimation, Speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving Cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

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“…Current conventional SAR moving target shadow detectors have low precision due to their incomplete feature-extraction capacities between complex scenes. To alleviate this issue, the contribution by Bao et al [15] presented a new DLN called ShadowDeNet. This network was invented for better shadow detection of moving ground targets on signal SAR data.…”

Section: Overview Of Contributionsmentioning

confidence: 99%