Improved Object Detection using Data Enhancement method based on Generative Adversarial Nets

Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.

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Moving Object Detection and Shadow Removing under Changing Illumination Condition

Mathematical Problems in Engineering ◽

10.1155/2014/827461 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Jinhai Xiang ◽

Heng Fan ◽

Honghong Liao ◽

Jun Xu ◽

Weiping Sun ◽

...

Keyword(s):

Object Detection ◽

Intensity Ratio ◽

Moving Objects ◽

Spatial Relation ◽

Gaussian Mixture ◽

Moving Object Detection ◽

Moving Object ◽

Illumination Change ◽

Enhancement Method ◽

Local Intensity

Moving object detection is a fundamental step in video surveillance system. To eliminate the influence of illumination change and shadow associated with the moving objects, we proposed a local intensity ratio model (LIRM) which is robust to illumination change. Based on the analysis of the illumination and shadow model, we discussed the distribution of local intensity ratio. And the moving objects are segmented without shadow using normalized local intensity ratio via Gaussian mixture model (GMM). Then erosion is used to get the moving objects contours and erase the scatter shadow patches and noises. After that, we get the enhanced moving objects contours by a new contour enhancement method, in which foreground ratio and spatial relation are considered. At last, a new method is used to fill foreground with holes. Experimental results demonstrate that the proposed approach can get moving objects without cast shadow and shows excellent performance under various illumination change conditions.

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Lightweight Feature Enhancement Network for Single-Shot Object Detection

Sensors ◽

10.3390/s21041066 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1066

Author(s):

Peng Jia ◽

Fuxiang Liu

Keyword(s):

Receptive Field ◽

Object Detection ◽

Real Time ◽

Detection System ◽

Field Enhancement ◽

Detection Performance ◽

Feature Representation ◽

Data Sets ◽

Single Shot ◽

Enhancement Method

At present, the one-stage detector based on the lightweight model can achieve real-time speed, but the detection performance is challenging. To enhance the discriminability and robustness of the model extraction features and improve the detector’s detection performance for small objects, we propose two modules in this work. First, we propose a receptive field enhancement method, referred to as adaptive receptive field fusion (ARFF). It enhances the model’s feature representation ability by adaptively learning the fusion weights of different receptive field branches in the receptive field module. Then, we propose an enhanced up-sampling (EU) module to reduce the information loss caused by up-sampling on the feature map. Finally, we assemble ARFF and EU modules on top of YOLO v3 to build a real-time, high-precision and lightweight object detection system referred to as the ARFF-EU network. We achieve a state-of-the-art speed and accuracy trade-off on both the Pascal VOC and MS COCO data sets, reporting 83.6% AP at 37.5 FPS and 42.5% AP at 33.7 FPS, respectively. The experimental results show that our proposed ARFF and EU modules improve the detection performance of the ARFF-EU network and achieve the development of advanced, very deep detectors while maintaining real-time speed.

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Training Object Detection And Recognition CNN Models Using Data Augmentation

Electronic Imaging ◽

10.2352/issn.2470-1173.2017.10.imawm-163 ◽

2017 ◽

Vol 2017 (10) ◽

pp. 27-36 ◽

Cited By ~ 16

Author(s):

Daniel Mas Montserrat ◽

Qian Lin ◽

Jan Allebach ◽

EdwardJ. Delp

Keyword(s):

Object Detection ◽

Data Augmentation ◽

Using Data ◽

Detection And Recognition

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Object Detection in Artworks Using Data Augmentation

2020 International Conference on Information and Communication Technology Convergence (ICTC) ◽

10.1109/ictc49870.2020.9289321 ◽

2020 ◽

Author(s):

Hyeong-Ju Jeon ◽

Soonchul Jung ◽

Yoon-Seok Choi ◽

Jae Woo Kim ◽

Jin Seo Kim

Keyword(s):

Object Detection ◽

Data Augmentation ◽

Using Data

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Small Object Detection in Traffic Scenes Based on YOLO-MXANet

Sensors ◽

10.3390/s21217422 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7422

Author(s):

Xiaowei He ◽

Rao Cheng ◽

Zhonglong Zheng ◽

Zeji Wang

Keyword(s):

Object Detection ◽

Spatial Attention ◽

Training Model ◽

Activation Function ◽

Model Complexity ◽

Detection Accuracy ◽

Small Object ◽

Enhancement Method ◽

Detection Speed ◽

Improved Algorithm

In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is utilized to improve loss function for promoting the positioning accuracy of the small object. In order to reduce the complexity of the model, we present a lightweight yet powerful backbone network (named SA-MobileNeXt) that incorporates channel and spatial attention. Our approach can extract expressive features more effectively by applying the Shuffle Channel and Spatial Attention (SCSA) module into the SandGlass Block (SGBlock) module while increasing the parameters by a small number. In addition, the data enhancement method combining Mosaic and Mixup is employed to improve the robustness of the training model. The Multi-scale Feature Enhancement Fusion (MFEF) network is proposed to fuse the extracted features better. In addition, the SiLU activation function is utilized to optimize the Convolution-Batchnorm-Leaky ReLU (CBL) module and the SGBlock module to accelerate the convergence of the model. The ablation experiments on the KITTI dataset show that each improved method is effective. The improved algorithm reduces the complexity and detection speed of the model while improving the object detection accuracy. The comparative experiments on the KITTY dataset and CCTSDB dataset with other algorithms show that our algorithm also has certain advantages.

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Using hyperspectral image enhancement method for small size object detection on the sea surface

10.1117/12.2194606 ◽

2015 ◽

Cited By ~ 3

Author(s):

Lu Yan ◽

Naoki Noro ◽

Yohei Takara ◽

Fuminori Ando ◽

Masahiro Yamaguchi

Keyword(s):

Object Detection ◽

Image Enhancement ◽

Hyperspectral Image ◽

Sea Surface ◽

Enhancement Method

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Object detection of mobile robot using data-mining algorithm

10.1109/iccas.2007.4406670 ◽

2007 ◽

Cited By ~ 1

Author(s):

Jin-Seob Lee ◽

Ji-Wook Kwon ◽

Dongkyoung Chwa ◽

Suk-Kyo Hong

Keyword(s):

Data Mining ◽

Mobile Robot ◽

Object Detection ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Using Data

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Enhancing action recognition of construction workers using data-driven scene parsing

Journal of Civil Engineering and Management ◽

10.3846/jcem.2018.6133 ◽

2018 ◽

Vol 24 (7) ◽

pp. 568-580 ◽

Cited By ~ 1

Author(s):

Jun Yang

Keyword(s):

Object Detection ◽

Action Recognition ◽

Semantic Information ◽

Parameter Tuning ◽

Construction Workers ◽

Data Driven ◽

Local Context ◽

Construction Sites ◽

Scene Parsing ◽

Using Data

Vision-based action recognition of construction workers has attracted increasing attention for its diverse applications. Though state-of-the-art performances have been achieved using spatial-temporal features in previous studies, considerable challenges remain in the context of cluttered and dynamic construction sites. Considering that workers actions are closely related to various construction entities, this paper proposes a novel system on enhancing action recognition using semantic information. A data-driven scene parsing method, named label transfer, is adopted to recognize construction entities in the entire scene. A probabilistic model of actions with context is established. Worker actions are first classified using dense trajectories, and then improved by construction object recognition. The experimental results on a comprehensive dataset show that the proposed system outperforms the baseline algorithm by 10.5%. The paper provides a new solution to integrate semantic information globally, other than conventional object detection, which can only depict local context. The proposed system is especially suitable for construction sites, where semantic information is rich from local objects to global surroundings. As compared to other methods using object detection to integrate context information, it is easy to implement, requiring no tedious training or parameter tuning, and is scalable to the number of recognizable objects.

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