Adaptive AFM imaging based on object detection using compressive sensing

Micron ◽  
2021 ◽  
pp. 103197
Author(s):  
Guoqiang Han ◽  
Yongjian Chen ◽  
Teng Wu ◽  
Huaidong Li ◽  
Jian Luo
Keyword(s):  
Object Detection ◽  
Compressive Sensing ◽  
Afm Imaging

Compressive sensing ghost imaging object detection using generative adversarial networks

2019 ◽  
Vol 58 (01) ◽  
pp. 1 ◽  
Author(s):  
Xiang Zhai ◽  
Zhengdong Cheng ◽  
Yuan Wei ◽  
Zhenyu Liang ◽  
Yi Chen
Keyword(s):  
Object Detection ◽  
Compressive Sensing ◽  
Generative Adversarial Networks ◽  
Ghost Imaging ◽  
Adversarial Networks

scholarly journals Real-Time and Deep Learning Based Vehicle Detection and Classification Using Pixel-Wise Code Exposure Measurements

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1014 ◽  
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bryan Chou ◽  
Bence Budavari ◽  
Jude Larkin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Compressive Sensing ◽  
Real Time ◽  
Classification Performance ◽  
Video Data ◽  
Real Time System ◽  
Processing Power ◽  
Compressed Data ◽  
Processing Device

One key advantage of compressive sensing is that only a small amount of the raw video data is transmitted or saved. This is extremely important in bandwidth constrained applications. Moreover, in some scenarios, the local processing device may not have enough processing power to handle object detection and classification and hence the heavy duty processing tasks need to be done at a remote location. Conventional compressive sensing schemes require the compressed data to be reconstructed first before any subsequent processing can begin. This is not only time consuming but also may lose important information in the process. In this paper, we present a real-time framework for processing compressive measurements directly without any image reconstruction. A special type of compressive measurement known as pixel-wise coded exposure (PCE) is adopted in our framework. PCE condenses multiple frames into a single frame. Individual pixels can also have different exposure times to allow high dynamic ranges. A deep learning tool known as You Only Look Once (YOLO) has been used in our real-time system for object detection and classification. Extensive experiments showed that the proposed real-time framework is feasible and can achieve decent detection and classification performance.

Super-resolution AFM imaging based on compressive sensing

2020 ◽  
Vol 508 ◽  
pp. 145231
Author(s):  
Guoqiang Han ◽  
Luyao Lv ◽  
Gaopeng Yang ◽  
Yixiang Niu
Keyword(s):  
Compressive Sensing ◽  
Super Resolution ◽  
Afm Imaging

scholarly journals An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression

2019 ◽  
Vol 33 ◽  
pp. 5949-5956 ◽  
Author(s):  
Xichuan Zhou ◽  
Lang Xu ◽  
Shujun Liu ◽  
Yingcheng Lin ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Image Compression ◽  
Object Detection ◽  
Compressive Sensing ◽  
Sufficient Condition ◽  
Data Embedding ◽  
Convolutional Network ◽  
Convolution Operation ◽  
Computational Overhead ◽  
Convolution Kernels ◽  
Image Set

This paper addresses the challenge of designing efficient framework for real-time object detection and image compression. The proposed Compressive Convolutional Network (CCN) is basically a compressive-sensing-enabled convolutional neural network. Instead of designing different components for compressive sensing and object detection, the CCN optimizes and reuses the convolution operation for recoverable data embedding and image compression. Technically, the incoherence condition, which is the sufficient condition for recoverable data embedding, is incorporated in the first convolutional layer of the CCN model as regularization; Therefore, the CCN convolution kernels learned by training over the VOC and COCO image set can be used for data embedding and image compression. By reusing the convolution operation, no extra computational overhead is required for image compression. As a result, the CCN is 3.1 to 5.0 fold more efficient than the conventional approaches. In our experiments, the CCN achieved 78.1 mAP for object detection and 3.0 dB to 5.2 dB higher PSNR for image compression than the examined compressive sensing approaches.

scholarly journals Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2079 ◽  
Author(s):  
Longlong Liao ◽  
Kenli Li ◽  
Canqun Yang ◽  
Jie Liu
Keyword(s):  
Object Detection ◽  
Compressive Sensing ◽  
Large Scale ◽  
Low Cost ◽  
Surveillance Systems ◽  
Storage Space ◽  
Multiple Measurement ◽  
Transmission Bandwidth ◽  
Comprehensive Evaluations ◽  
And Storage

When measurement rates grow, most Compressive Sensing (CS) methods suffer from an increase in overheads of transmission and storage of CS measurements, while reconstruction quality degrades appreciably when measurement rates reduce. To solve these problems in real scenarios such as large-scale distributed surveillance systems, we propose a low-cost image CS approach called MRCS for object detection. It predicts key objects using the proposed MYOLO3 detector, and then samples the regions of the key objects as well as other regions using multiple measurement rates to reduce the size of sampled CS measurements. It also stores and transmits half-precision CS measurements to further reduce the required transmission bandwidth and storage space. Comprehensive evaluations demonstrate that MYOLO3 is a smaller and improved object detector for resource-limited hardware devices such as surveillance cameras and aerial drones. They also suggest that MRCS significantly reduces the required transmission bandwidth and storage space by declining the size of CS measurements, e.g., mean Compression Ratios (mCR) achieves 1.43–22.92 on the VOC-pbc dataset. Notably, MRCS further reduces the size of CS measurements by half-precision representations. Subsequently, the required transmission bandwidth and storage space are reduced by one half as compared to the counterparts represented with single-precision floats. Moreover, it also substantially enhances the usability of object detection on reconstructed images with half-precision CS measurements and multiple measurement rates as compared to its counterpart, using a single low measurement rate.

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