Faster R-CNN for marine organisms detection and recognition using data augmentation

Huang, Hai; Zhou, Hao; Yang, Xu; Zhang, Lu; Qi, Lu; Zang, Ai-Yun

doi:10.1016/j.neucom.2019.01.084

Cited by 143 publications

(56 citation statements)

References 12 publications

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“…This modification has increased the speed of the detection with better detection accuracy where reported miss rate was 10.5% at detection speed of 25 frames per second. It worth mentioned that YOLO-based methods have reported a higher speed up rate as compared with Faster R-CNN model but less detection accuracy [14].…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

et al. 2020

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Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learningbased models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

et al. 2020

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“…Afterward, [5] introduced a region proposal network (RPN) that shared features with a detection network to produce region proposals. [17] combined Faster R-CNN [5] with several data augmentation methods for marine organisms detection. [18] proposed position-sensitive score maps to address the dilemma between translationinvariance in classification and translation-variance in localization.…”

Section: A Two-stage Detectorsmentioning

confidence: 99%

Auxiliary Detection Head for One-Stage Object Detection

Jin

Taniguchi

2020

IEEE Access

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The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field. In this paper, we propose an auxiliary detection head to boost the performance of one-stage object detectors. Similar to other detection heads, the auxiliary detection head consists of a classification subnet and a regression subnet, which are essentially two convolution layers. Thus the auxiliary detection head is computationally efficient. Besides, the auxiliary detection head achieves implicit two-step cascaded regression. Specifically, the auxiliary detection head uses its output boxes as anchors for further regression. Within the auxiliary detection head, refinement of object localization corresponds to adjust the positions of its output boxes towards ground truth boxes, which helps the network learn more robust features. At inference, the auxiliary detection head can be removed without any adverse effect on the performance of the main detector head, which benefits from its independence and leads to two advantages: shrink the model size and shorten inference time. The proposed method is evaluated on Pascal VOC and COCO datasets. By incorporating the auxiliary detection head into a state-of-the-art object detector in parallel with the main detection head, we show consistent improvement over its performance on different benchmarks, whereas no extra parameters are introduced at inference time. INDEX TERMS Auxiliary detection head, two-step cascaded regression, convolutional neural networks, object detection. The associate editor coordinating the review of this manuscript and approving it for publication was Wei Liu.

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“…Recently, deep learning through deep convolutional networks (CNNs) show promising results in object detection and classification (Chen, Li & Li, 2020). The deep CNN approach has been gained significant improvement in face detection (Garcia & Delakis, 2002;Osadchy, Cun & Miller, 2007), facial point detection (Sun, Wang & Tang 2013), human attribute inference (Zhang, Paluri, Ranzato, Darrell & Bourdev, 2014), plant phenotyping (Jiang & Li, 2020), organism detection (Huang et al, 2019) and in many other applications. These models achieve state-of-the-art results on several data sets and can enhance the success rate of criminal identification as compared to conventional as well as other shallow machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Crime Detection and Criminal Recognition to Intervene in Interpersonal Violence Using Deep Convolutional Neural Network With Transfer Learning

Haque

Hafiz

Azad

et al. 2021

International Journal of Ambient Computing and Intelligence

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Interpersonal violence, such as physical and sexual abuse, eve-teasing, bullying, and taking hostages, is a growing concern in our society. The criminals who directly or indirectly committed the crime often do not go into the trial for the lack of proper evidence as it is very tough to collect photographic proof of the incident. A subject's corneal reflection has the potentiality to reveal the bystander images. Motivated with this clue, a novel approach is proposed in the current paper that uses a convolutional neural network (CNN) along with transfer learning in identifying crime as well as recognizing the criminals from the corneal reflected image of the victim called the Purkinje image. This study found that off-the-shelf CNN can be fine-tuned to extract discriminative features from very low resolution and noisy images. The procedure is validated using the developed datasets comprising six different subjects taken at diverse situations. They confirmed that it has the ability to recognize criminals from corneal reflection images with an accuracy of 95.41%.

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Faster R-CNN for marine organisms detection and recognition using data augmentation

Cited by 143 publications

References 12 publications

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Auxiliary Detection Head for One-Stage Object Detection

Crime Detection and Criminal Recognition to Intervene in Interpersonal Violence Using Deep Convolutional Neural Network With Transfer Learning

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