Deep Learning Based Multiple Object Detection

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.

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Embedded multiple object detection based on deep learning technique for advanced driver assistance system

2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas.2017.8052888 ◽

2017 ◽

Cited By ~ 1

Author(s):

Fong-An Chang ◽

Chia-Chi Tsai ◽

Ching-Kan Tseng ◽

Jiun-In Guo

Keyword(s):

Deep Learning ◽

Object Detection ◽

Driver Assistance System ◽

Assistance System ◽

Advanced Driver Assistance System ◽

Driver Assistance ◽

Multiple Object ◽

Learning Technique ◽

Multiple Object Detection

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Multiple Object Detection and Tracking Using Deep Learning

Lecture Notes in Electrical Engineering - Proceedings of International Conference on Communication, Circuits, and Systems ◽

10.1007/978-981-33-4866-0_32 ◽

2021 ◽

pp. 257-263

Author(s):

Shreyas Burde ◽

Suneeta V. Budihal

Keyword(s):

Deep Learning ◽

Object Detection ◽

Object Detection And Tracking ◽

Detection And Tracking ◽

Multiple Object ◽

Multiple Object Detection

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421500385 ◽

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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