Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

Dinakaran, Ranjith; Easom, Philip; Zhang, Li; Bouridane, Ahmed; Jiang, Richard; Edirisinghe, Eran A.

doi:10.1109/ijcnn.2019.8851859

Cited by 7 publications

(11 citation statements)

References 23 publications

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“…It produces images of pedestrians in unusual scenarios and positions, helping traditional object detection models to improve their ability to detect pedestrians. Dinakaram and colleagues [17][18][19] used another known and proven ability of Deep Convolutional GAN: an image resolution improvement. All three works combine the GAN's image resolution improvement with SSD to increase its pedestrian detection capabilities in different sizes and distances.…”

Section: Resultsmentioning

confidence: 99%

2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

Bertolini¹,

Monteiro²

2022

JBTH

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Object Detection (OD) is one of the most critical tasks in 2D image processing. The researchers proposed multiple math models and frameworks based on Deep Convolutional Networks, such as R-CNN, SSD, and YOLO are the most common. Generative Adversarial Nets (GAN) represent a prominent field of study in machine learning, and it has been applied to many tasks with exciting results. This work aims to assess the potential of GANs applied to OD tasks and the proposed frameworks as a field of study. The methodology used was a systemic review of 14 papers. The conclusion shows that although OD and GANs are popular themes, there are not many developments in the intersection of both subjects. Therefore, OD with GAN-applied tasks is an excellent field to explore in future works.

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Section: Resultsmentioning

confidence: 99%

2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

Bertolini¹,

Monteiro²

2022

JBTH

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“…Pedestrian Detection [16] GAN for synthetic data creation [17] DCGAN + SSD [18] DCGAN + SSD [19] DCGAN + SSD Small Object Detection [20] Faster R-CNN + GAN [21] GAN + CNN + SSD/Faster R-CNN [22] CNN + ResNet + GAN Unsupervised Bounding Box Detection [23] CNN + GAN + Reinforcement Learning [24] Dilated CNN + GAN with Mask Mean Loss [25] Encoder + Conditional GAN 2.56 SSD300 Pascal VOC 2007 [16] Not Applicable Not Applicable Not Applicable [17] 45.2 SSD CIFAR-10/100 [18] 39.4 SSD VOC [19] Not Applicable Not Applicable Not Applicable [20] 19.47 Faster R-CNN Tsinghua-Tencent 100K [21] 25.1 FRCNN COWC Dataset [22] 60 Faster R-CNN (Small Objects) Tsinghua-Tencent 100K [23] Not Applicable Not Applicable Not Applicable [24] 5.37 [23] Car (Stanford) [25] 2.6 WCCN VGG16 VOC2007…”

Section: International Symposium On Innovation and Technology (Siintec)mentioning

confidence: 99%

“…[17], [18] and [19] use another very known and proven ability of Deep Convolutional GAN's which is images resolution improvement. All the three works combine the GAN's image resolution improvement with SSD to increase its pedestrian detection capabilities in different sizes and distances.…”

Section: International Symposium On Innovation and Technology (Siintec)mentioning

confidence: 99%

2d Image Object Detection Aided by Generative Adversarial Networks a Literature Review

Bertolini¹,

Monteiro²

2021

Blucher Engineering Proceedings

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Object Detection (OD) is one of the most important tasks in 2D image processing. Multiple math models have been proposed and frameworks based in Deep Convolutional Networks such as R-CNN, SSD and YOLO are most common. Generative Adversarial Nets (GAN's) represent a prominent field of study in machine learning and it has been applied to many tasks with exciting results. The objective of this work is to assess the potential of GAN's applied to OD tasks and the proposed frameworks as field of study. The methodology used was a systemically review of 14 papers. The conclusion shows that even though OD and GAN's are popular themes, there are not many developments done in the intersection of both subjects. Therefore, OD with GAN applied tasks are an excellent field to explore in future works.

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“…In our recent work [11], it was evidenced that image quality can critically impact on object detection. In [12], we exploited a DCGAN (deep convolutional generative adversarial network) [13] for image enhancement, as shown in Figure 2, and we found that such an enhancement scheme could help alleviate the serious degradation of object detection caused by low-quality images. In this paper, we aimed to leverage our initial discovery and apply such a DCGAN-based framework [12] for undersea/subsea object detection using a single-shot detector (SSD) [14].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], we exploited a DCGAN (deep convolutional generative adversarial network) [13] for image enhancement, as shown in Figure 2, and we found that such an enhancement scheme could help alleviate the serious degradation of object detection caused by low-quality images. In this paper, we aimed to leverage our initial discovery and apply such a DCGAN-based framework [12] for undersea/subsea object detection using a single-shot detector (SSD) [14]. This combinational framework DCGAN+SSD can help object detection in underwater environments by engaging DCGAN for image conversion, after which the object detector is applied to the converted images.…”

Section: Introductionmentioning

confidence: 99%

Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection

Dinakaranr

Zhang

et al. 2022

Remote Sensing

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Undersea/subsea data collection via automated underwater vehicles (AUVs) plays an important role for marine biodiversity research, while it is often much more challenging than the data collection above ground via satellites or AUVs. To enable the automated undersea/subsea data collection system, the AUVs are expected to be able to automatically track the objects of interest through what they can “see” from their mounted underwater cameras, where videos or images could be drastically blurred and degraded in underwater lighting conditions. To solve this challenge, in this work, we propose a cascaded framework by combining a DCGAN (deep convolutional generative adversarial network) with an object detector, i.e., single-shot detector (SSD), named DCGAN+SSD, for the detection of various underwater targets from the mounted camera of an automated underwater vehicle. In our framework, our assumption is that DCGAN can be leveraged to alleviate the impact of underwater conditions and provide the object detector with a better performance for automated AUVs. To optimize the hyperparameters of our models, we applied a particle swarm optimization (PSO)-based strategy to improve the performance of our proposed model. In our experiments, we successfully verified our assumption that the DCGAN+SSD architecture can help improve the object detection toward the undersea conditions and achieve apparently better detection rates over the original SSD detector. Further experiments showed that the PSO-based optimization of our models could further improve the model in object detection toward a more robust and fair performance, making our work a promising solution for tackling the challenges in AUVs.

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Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks

Cited by 7 publications

References 23 publications

2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

2d Image Object Detection Aided by Generative Adversarial Networks a Literature Review

Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection

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