Ensemble feature learning for material recognition with convolutional neural networks

Bian, Peng; Li, Wanwan; Jin, Yi; Zhi, Ruicong

doi:10.1186/s13640-018-0300-z

Cited by 14 publications

(12 citation statements)

References 29 publications

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“…A proposed solution to that problem is by using a deep convolutional neural network (DCNN) to describe the material of the image where the optimal description of the image is learned by backpropagation. In the work [8], the authors used CNN to extract features from a material dataset and then used ensemble learning of knowledge-based classifiers to classify the materials that make up the image, they achieved a mean accuracy of 85%. In their work [9], the authors proposed a transfer learning-based approach to classify the "FMD" material dataset, they used the knowledge from LeNET by copying the first convolutional layers while fine-tuning the other remaining layers.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

et al. 2022

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The accumulation of plastic objects in the Earth’s environment will adversely affect wildlife, wildlife habitat, and humans. The huge amount of unrecycled plastic ends up in landfill and thrown into unregulated dump sites. In many cases, specifically in the developing countries, plastic waste is thrown into rivers, streams and oceans. In this work, we employed the power of deep learning techniques in image processing and classification to recognize plastic waste. Our work aims to identify plastic texture and plastic objects in images in order to reduce plastic waste in the oceans, and facilitate waste management. For this, we use transfer learning in two ways: in the first one, a pre-trained CNN model on ImageNet is used as a feature extractor, then an SVM classifier for classification, the second strategy is based on fine tuning the pre-trained CNN model. Our approach was trained and tested using two (02) challenging datasets one is a texture recognition dataset and the other is for object detection, and achieves very satisfactory results using two (02) deep learning strategies.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

et al. 2022

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“…Instead, the algorithm learns the features required for recognition. Indeed, modern methods using CNNs showed significantly better results [10,18].…”

Section: Related Workmentioning

confidence: 99%

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

Großmann

Horn

Niggemann

2022

Preprint

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Material recognition using optical sensors is a key enabler technology in the field of automation. Nowadays, in the age of deep learning, the challenge shifted from (manual) feature engineering to collecting big data. State of the art recognition approaches are based on deep neural networks employing huge databases. But still, it is difficult to transfer these latest recognition results into the wild – various lighting conditions, a changing image quality, or different and new material classes are challenging complications. Evaluating a larger electromagnetic spectrum is one way to solve these challenges. In this study, the infrared (IR) emissivity as a materialspecific property is investigated regarding its suitability for increasing the material classification reliability. Predictions of a deep learning model are combined with engineered features from IR data. This approach increases the overall accuracy and helps to differentiate between materials that visually appear similar. The solution is verified using real data from the field of automatized disinfection processes.

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“…Based on the vehicle speed and acceleration, Kumagai established a driving model to predict whether the driver could stop in front of the red lamps with Dynamic Bayesian Networks [32]. In addition, fuzzy neural networks and fuzzy inference methods have been widely used in driver behavior analyses [33,34]. Among them, the support vector machine (SVM) has a good application effect.…”

Section: Introductionmentioning

confidence: 99%

A driver’s car-following behavior prediction model based on multi-sensors data

Wang

et al. 2020

J Wireless Com Network

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The prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver's car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

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Ensemble feature learning for material recognition with convolutional neural networks

Cited by 14 publications

References 29 publications

A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

A Deep Learning Approach to Manage and Reduce Plastic Waste in the Oceans

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

A driver’s car-following behavior prediction model based on multi-sensors data

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