Automatic Hierarchical Classification of Kelps Using Deep Residual Features

Mahmood, Ammar; Ospina, Ana Giraldo; Bennamoun, Mohammed; An, Senjian; Sohel, Ferdous; Boussaid, Farid; Hovey, Renae K.; Fisher, Robert B.; Kendrick, Gary A.

doi:10.3390/s20020447

Cited by 44 publications

(26 citation statements)

References 46 publications

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“…It saves computational time for extracting features from the image. The input images were first passed through the ResNet pre-trained architecture, which extracts the important low dimensional features from the image, and then it is passed to the convolutional layers of Retina Net for computing the class of object and drawing a bounding box around the particular object of interest, ResNet-50 architecture [20] and RetnaNet architecture [21]. Figure 4 depicts the Model2 architecture.…”

Section: Model2: Retina Net With Resnet-50 Backendmentioning

confidence: 99%

Evaluating One Stage Detector Architecture of Convolutional Neural Network for Threat Object Detection Using X-Ray Baggage Security Imaging

Malarvizhi¹,

Rajaduari²,

Choudhury³

et al. 2020

RIA

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Neural networks can map complex functions between input and a target, and they have produced state-of-the-art results in the field of computer vision. These neural network based models have superseded the conventional computer vision algorithms for X-ray imaging. In this paper, we propose a deep neural network based solution for a subset of the X-ray imaging problem of detecting sharp items in a baggage X-ray. Existing reports were region based CNN architecture for an object detection in X-ray imaging systems. We propose Deep learning method as a Single Shot Detector (SSD) and RetinaNet, which are a oneshot technique for object detection and are able to do inference in real time 15-30 frame per seconds (fps) videos. These techniques are Fully Convolutional Network (FCN) and have the capability to do both classification and regression with the same shared weights. These networks return a bounding box around the object of interest along with the class of that particular object. This technique has been used in training single stage detectors for four objects of interest-knife, scissors, wrench and pliers. We have achieved good detection accuracy with mean average precision of a 60.5% for SSD and of 60.9% for RetinaNet using the SIX-ray10 database, which contains harmful items and non-harmful items. The ratio of number of harmful to non-harmful items is very low, making the problem a daunting one. Through various experimentations we have come up with the best possible results using various pre-trained networks as the feature extractor in tandem with these object detection algorithms. With further improvements on the achieved results, it would be possible to deploy this technique in airports to minimize human error and improve security in such environments.

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Section: Model2: Retina Net With Resnet-50 Backendmentioning

confidence: 99%

Evaluating One Stage Detector Architecture of Convolutional Neural Network for Threat Object Detection Using X-Ray Baggage Security Imaging

Malarvizhi¹,

Rajaduari²,

Choudhury³

et al. 2020

RIA

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“…Dengshan Li et al [25] proposed a method for detecting rice diseases and insect pests based on deep convolutional neural networks. Ammar Mahmood et al [26] used the deep residual function to automatically classify kelp. These works strongly demonstrated the powerful role of deep learning algorithms in various research fields.…”

Section: Introductionmentioning

confidence: 99%

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

Wang

Huang

et al. 2020

Sensors

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Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

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“…Mahmood et al (2020) 57 applied computerized DL characterization of annotated kelp species. They presented an automatic hierarchical classification method to classify kelps in collected images.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network recognition of shallow water corals in the Gulf of Eilat (Aqaba)

Raphael

Dubinsky

Iluz

et al. 2020

Sci Rep

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We describe the application of the computerized deep learning methodology to the recognition of corals in a shallow reef in the Gulf of Eilat, Red Sea. This project is aimed at applying deep neural network analysis, based on thousands of underwater images, to the automatic recognition of some common species among the 100 species reported to be found in the Eilat coral reefs. This is a challenging task, since even in the same colony, corals exhibit significant within-species morphological variability, in terms of age, depth, current, light, geographic location, and inter-specific competition. Since deep learning procedures are based on photographic images, the task is further challenged by image quality, distance from the object, angle of view, and light conditions. We produced a large dataset of over 5,000 coral images that were classified into 11 species in the present automated deep learning classification scheme. We demonstrate the efficiency and reliability of the method, as compared to painstaking manual classification. Specifically, we demonstrated that this method is readily adaptable to include additional species, thereby providing an excellent tool for future studies in the region, that would allow for real time monitoring the detrimental effects of global climate change and anthropogenic impacts on the coral reefs of the Gulf of Eilat and elsewhere, and that would help assess the success of various bioremediation efforts.

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Automatic Hierarchical Classification of Kelps Using Deep Residual Features

Cited by 44 publications

References 46 publications

Evaluating One Stage Detector Architecture of Convolutional Neural Network for Threat Object Detection Using X-Ray Baggage Security Imaging

Evaluating One Stage Detector Architecture of Convolutional Neural Network for Threat Object Detection Using X-Ray Baggage Security Imaging

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods

Deep neural network recognition of shallow water corals in the Gulf of Eilat (Aqaba)

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