Hidden object detection for classification of threat

2017 ◽  
Author(s):  
K.S. Gautam ◽  
Senthil Kumar Thangavel
Keyword(s):  
Object Detection

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

scholarly journals Optimized loss functions for object detection and application on nighttime vehicle detection

2021 ◽  
pp. 095440702110363
Author(s):  
Shang Jiang ◽  
Haoran Qin ◽  
Bingli Zhang ◽  
Jieyu Zheng
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Mahalanobis Distance ◽  
Vehicle Detection ◽  
Detection Task ◽  
Loss Functions ◽  
Detection Accuracy ◽  
Localization Accuracy ◽  
Detection Speed

The loss function is a crucial factor that affects the detection precision in the object detection task. In this paper, we optimize both two loss functions for classification and localization simultaneously. Firstly, we reconstruct the classification loss function by combining the prediction results of localization, aiming to establish the correlation between localization and classification subnetworks. Compared to the existing studies, in which the correlation is only established among the positive samples and applied to improve the localization accuracy of predicted boxes, this paper utilizes the correlation to define the hard negative samples and then puts emphasis on the classification of them. Thus the whole misclassified rate for negative samples can be reduced. Besides, a novel localization loss named MIoU is proposed by incorporating a Mahalanobis distance between the predicted box and target box, eliminating the gradients inconsistency problem in the DIoU loss, further improving the localization accuracy. Finally, the proposed methods are applied to train the networks for nighttime vehicle detection. Experimental results show that the detection accuracy can be outstandingly improved with our proposed loss functions without hurting the detection speed.

Hidden object detection for classification of threat

2020 ◽  
Vol 13 (1/2) ◽  
pp. 217
Author(s):  
K.S. Gautam ◽  
Senthil Kumar Thangavel
Keyword(s):  
Object Detection

scholarly journals Comparison of Object Detection Approaches Applied to Field Images of Papanicolaou Stained Cytology Slides

2021 ◽  
Author(s):  
André Victória Matias ◽  
Allan Cerentini ◽  
Luiz Antonio Buschetto Macarini ◽  
João Gustavo Atkinson Amorim ◽  
Felipe Perozzo Daltoé ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Oral Cancer ◽  
Global Health ◽  
Object Detection ◽  
Learning Approach ◽  
Non Invasive ◽  
Global Health Issue ◽  
Invasive Method

Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, that can also be useful to detect cancer on oral cavities. Although oral cancer is considered a global health issue with 350.000 people diagnosed over a year it can successfully be treated if diagnosed at early stages. The manual process of analyzing cells to detect abnormalities is time-consuming and subject to variations in perceptions from different professionals. To evaluate a possible solution to the automation of this process, in this paper we employ the object detection deep learning approach in the analysis of this type of image using 3 models: RetinaNet, Faster R-CNN, and Mask R-CNN. We trained and tested the models using images from 6 cytology slides (4 cancer cases and 2 healthy samples) and our results show that Mask R-CNN was the best model for localization and classification of nuclei with an IoU of 0.51 and recall of abnormal nuclei of 0.67.

scholarly journals ON THE FUSION OF CAMERA AND LIDAR FOR 3D OBJECT DETECTION AND CLASSIFICATION

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 149-156
Author(s):  
N. Kozonek ◽  
N. Zeller ◽  
H. Bock ◽  
M. Pfeifle
Keyword(s):  
Object Detection ◽  
Autonomous Driving ◽  
Real World Data ◽  
Separate Object ◽  
Centroid Estimation ◽  
Rgb Images ◽  
Bounding Boxes ◽  
Cross Calibration ◽  
3D Object Detection

<p><strong>Abstract.</strong> In this paper we present a sensor fusion framework for the detection and classification of objects in autonomous driving applications. The presented method uses a state-of-the-art convolutional neural network (CNN) to detect and classify object from RGB images. The 2D bounding boxes calculated by the CNN are fused with the 3D point cloud measured by Lidar sensors. An accurate sensor cross-calibration is used to map the Lidar points into the image, where they are assigned to the 2D bounding boxes. A one-dimensional K-means algorithm is applied to separate object points from foreground and background and to calculated accurate 3D centroids for all detected objects. The proposed algorithm is tested based on real world data and shows a stable and reliable object detection and centroid estimation in different kind of situations.</p>

scholarly journals Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

2019 ◽  
Vol 10 (1) ◽  
pp. 87 ◽  
Author(s):  
Qingsheng Jiang ◽  
Dapeng Tan ◽  
Yanbiao Li ◽  
Shiming Ji ◽  
Chaopeng Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Engineering Application ◽  
Light Surface ◽  
Low Efficiency ◽  
Shaft Surface

Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

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