Crater Detection using Haar-Like Feature for Moon Landing System Based on the Surface Image

Tanaami, Takahiro; Takeda, Yuji; Aoyama, Norifumi; Mizumi, Syoto; Kamata, Hiroyuki; Takadama, Keiki; Ozawa, Shogo; Fukuda, Seisuke; Sawai, Shujiro

doi:10.2322/tastj.10.pd_39

Cited by 3 publications

(3 citation statements)

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“…Metode ini sering digunakan pada pendeteksian wajah [4]- [13]. Tetapi ada beberapa penelitian yang melakukan pelatihan khusus untuk haar cascade sehingga mampu untuk mendeteksi tomat [14] dan kawah pada bulan [15].…”

Section: A Pendahuluanunclassified

Deteksi Objek pada Pengambil Muatan Autonomous VTOL berdasarkan Fitur Warna dan Bentuk

Niam Tamami,

Mochamad Mobed Bachtiar,

Mohammad Syafrudin

et al. 2024

ijcs

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Dalam era modern, Autonomous VTOL (Vertical Take Of and Landing) telah mengambil peran yang semakin signifikan dalam berbagai aplikasi, salah satunya pengiriman barang. Keberhasilan proses pengambilan muatan ditentukan dari keberhasilan proses deteksi. Pada lingkungan terbuka, deteksi muatan sering kali tidak akurat karena adanya objek lain yang berada di atas tanah. Pada penelitian ini, proses deteksi didasarkan pada fitur warna dan bentuk dari objek untuk meningkatkan akurasi deteksi. Deteksi warna dilakukan dengan memberikan range batas nilai HSV dan menghitung luasan terbesar untuk mengetahui posisi muatan pada gambar. Selanjutnya deteksi Haar Cascade digunakan untuk memastikan apakah fitur bentuk objek sesuai dengan objek yang ingin dideteksi. Deteksi objek dengan hanya menggunakan Haar Cascade digunakan sebagai pembanding. Dari percobaan yang telah dilakukan, deteksi dengan menggunakan Haar Cascade didapatkan nilai akurasi 70.68%, sedangkan kombinasi deteksi kontur warna dan Haar Cascade didapatkan nilai akurasi yang lebih tinggi yaitu 87.93%.

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Section: A Pendahuluanunclassified

Deteksi Objek pada Pengambil Muatan Autonomous VTOL berdasarkan Fitur Warna dan Bentuk

Niam Tamami,

Mochamad Mobed Bachtiar,

Mohammad Syafrudin

et al. 2024

ijcs

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“…However, the rotation angle of the principal component data is limited to 0°, 90°, 180°, or 270°; thus, one principal component must be able to detect the crater that has a brightness direction in the 90° range. 4) To estimate the principal components, the crater images that are rotated to the left and right are summed together. The rotation range is 45° to the left and right, and the images are rotated in increments of 5°.…”

Section: Principal Components Of Crater Imagesmentioning

confidence: 99%

“…To meet this challenge, we earlier proposed a crater detection method based on Haar-like and LBP features, [1][2][3][4] which is applied to face detection from digital images. This method uses a number of rapid but weak object detectors.…”

Section: Introductionmentioning

confidence: 99%

Crater Detection Method using Principal Component Analysis and its Evaluation

Takino

Nomura

Moribe

et al. 2016

AEROSPACE TECHNOLOGY JAPAN

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In this study, a crater detection method for a moon-landing system with low computational resources is proposed. The proposed method is applied to the Smart Lander for Investigating Moon (SLIM), which aims for a pinpoint landing on the moon. According to this plan, surface images of the moon will be captured by a camera mounted on the space probe, and the craters are to be detected from the images. Based on the positional relationship between detected craters, the method estimates the exact flight position of the space probe. Because the computational resources of SLIM are limited, rapid and accurate crater detection must be performed using fixed-point arithmetic on a field-programmable gate array (FPGA). This study proposes a crater detection method that uses principal component analysis (PCA). The computational processing for crater detection by PCA is performed by product-sum operations, which are suitable for fixed-point arithmetic. Moreover, this method is capable of parallel processing; hence high-speed processing is expected. This study not only introduces a crater detection method using PCA but also evaluates the properties of this method.

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Position Identification Using Image Processing for UAV Flights in Martian Atmosphere

Higashino

Teruya

Yamada

2021

JRM

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This paper presents a method for the position identification of an unmanned aerial vehicle (UAV) in the Martian atmosphere in the future. It uses the image processing of craters captured via an onboard camera of the UAV and database images. The method is composed of two processes: individual crater detection using a cascade object detector and position identification using the recognition Taguchi (RT)-method. In crater detection, objects with shapes that resemble craters are detected regardless of their positions, and the positions of multiple detected craters are identified using the criterion variable D*, which is a normalized Mahalanobis distance. D* is calculated from several feature variables expressing the area ratios and relative positions of the detected craters in the RT-method.

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Crater Detection using Haar-Like Feature for Moon Landing System Based on the Surface Image

Cited by 3 publications

References 0 publications

Deteksi Objek pada Pengambil Muatan Autonomous VTOL berdasarkan Fitur Warna dan Bentuk

Deteksi Objek pada Pengambil Muatan Autonomous VTOL berdasarkan Fitur Warna dan Bentuk

Crater Detection Method using Principal Component Analysis and its Evaluation

Position Identification Using Image Processing for UAV Flights in Martian Atmosphere

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