Review on RGB-D Image Classification

Shuqin, 涂淑琴 Tu; Yueju, 薛月菊 Xue; Yun, 梁云 Liang; Ning, 黄宁 Huang; Xiao, 张晓 Zhang

doi:10.3788/lop53.060003

Cited by 3 publications

(1 citation statement)

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“…Spectral data have poor robustness in the case of target overlap, occlusion, large illumination changes, shadows, and complex scenes. Depth data that do not change with brightness and color can provide additional useful information for complex scenes (Shuqin et al, 2016). At present, the depth sensors on UAV platforms are mainly LiDAR (Wallace et al, 2012;Qiu et al, 2019;Tian et al, 2019;Wang D.Z.…”

Section: Light Detection and Rangingmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

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Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

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Section: Light Detection and Rangingmentioning

confidence: 99%

Applications of UAS in Crop Biomass Monitoring: A Review

et al. 2021

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RGB-D Indoor Object Recognition Algorithm Based on Fusion Convolutional Neural Network

Wang

Zhang

2019

J. Phys.: Conf. Ser.

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Aiming at the problems of insufficient network fusion and low detection efficiency in current object recognition using RGB-D images, a recognition algorithm based on the medium-level layer-by-layer fusion of dual-channel networks is proposed. First of all, the RGB and Depth networks are trained with ten labelled RGB-D indoor objects respectively, and then determine the fusion coefficients according to the identify accuracy of two types networks. Finally, two kinds of features are merged in convolutional layers step by step to obtain the final weights. By testing on the challenging NYU Depth v2 dataset, we found that the recognition accuracy of our method is 92.85%, and average detection time is 61.03ms per image. Through comparison experiments, we got the conclusion that average accuracy of the RGB-D layer-by-layer fusion network is 5.22% higher than that of the RGB network.

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