scholarly journals Multiple Object Detection Based on Clustering and Deep Learning Methods

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4424
Author(s):  
Huu Thu Nguyen ◽  
Eon-Ho Lee ◽  
Chul Hee Bae ◽  
Sejin Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Learning Methods ◽  
Multiple Object ◽  
Multiple Object Detection ◽  
Negative Effect

Multiple object detection is challenging yet crucial in computer vision. In This study, owing to the negative effect of noise on multiple object detection, two clustering algorithms are used on both underwater sonar images and three-dimensional point cloud LiDAR data to study and improve the performance result. The outputs from using deep learning methods on both types of data are treated with K-Means clustering and density-based spatial clustering of applications with noise (DBSCAN) algorithms to remove outliers, detect and cluster meaningful data, and improve the result of multiple object detections. Results indicate the potential application of the proposed method in the fields of object detection, autonomous driving system, and so forth.

A RGB-D Based Real-Time Multiple Object Detection and Ranging System for Autonomous Driving

2020 ◽  
Vol 20 (20) ◽  
pp. 11959-11966
Author(s):  
Jiachen Yang ◽  
Chenguang Wang ◽  
Huihui Wang ◽  
Qiang Li
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Multiple Object ◽  
Multiple Object Detection

Exploring Deep Learning Models for Overhead View Multiple Object Detection

2020 ◽  
Vol 7 (7) ◽  
pp. 5737-5744 ◽  
Author(s):  
Imran Ahmed ◽  
Sadia Din ◽  
Gwanggil Jeon ◽  
Francesco Piccialli
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Models ◽  
Multiple Object ◽  
Multiple Object Detection ◽  
Overhead View

scholarly journals Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1474 ◽  
Author(s):  
Muhammad Sualeh ◽  
Gon-Woo Kim
Keyword(s):  
Object Detection ◽  
Unscented Kalman Filter ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Multiple Model ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Multiple Object ◽  
Multiple Object Detection ◽  
Grid Based

Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.

scholarly journals Deep Learning Based Multiple Object Detection

2021 ◽  
Vol 1916 (1) ◽  
pp. 012225
Author(s):  
J Karthika ◽  
H Mohammed Imtiaz ◽  
M Deepakdharsan ◽  
B Akash ◽  
U Adimulam
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Multiple Object ◽  
Multiple Object Detection

Multiple-Object Detection and Segmentation Based on Deep Learning in High-Resolution Video Using Mask-RCNN

2021 ◽  
Author(s):  
Shaikh Shakil Abdul Rajjak ◽  
A. K. Kureshi
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Data Set ◽  
Training Time ◽  
Multiple Object ◽  
Proposed Model ◽  
Mixed Precision ◽  
Multiple Object Detection ◽  
Imaging Sensors

Imaging sensors with higher resolution and higher frame rates are becoming more popular for wide-area video surveillance (VS) and other applications as technology advances Using Mask-RCNN, we proposed Multiple-Object Detection and Segmentation in High-Resolution Video based on Deep Learning. The ResNet-50 ResNet-101 is used as the backbone in the proposed R-CNN Mask FPN model. The deep residual network’s design overcomes the problem of lower learning efficiency due to the network’s deepening. To reach the objective of the smallest overall error, the deep residual network divided the training series into one training block, minimizing the error of each block. It is roughly divided into five convolutional layer stages. The output scale is cut in half at each point. We used mixed precision FP16 and FP32 for training the model and achieved great speed in training time reduction in inference time for object. The COCO 2014 data set is used to train and validate the proposed model with mixed precision, leading to faster performance. The results of the experiments show that the proposed model can run at 30–48 frames per second with 85% accuracy.

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