Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

Hou, Yi; Zhang, Hong; Zhou, Shilin; Zou, Huanxin

doi:10.1155/2017/8104386

Cited by 12 publications

(8 citation statements)

References 35 publications

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“…Subsequently, to preserve exact spatial locations, a quantization‐free layer called RoI Align (Sun et al, 2018) is used for the region of interest (RoI) extracted via the RPN. RoI Align was proposed on the basis of RoI Pool (Hou et al, 2017; Ren et al, 2015) to obtain the image values of pixels with coordinates as floating point numbers by bilinear interpolation; this approach solves the problem of pixel‐to‐pixel misalignment between the network input and output.…”

Section: Experimental Model Testsmentioning

confidence: 99%

A deep learning technique based flood propagation experiment

Hou

Bai

et al. 2021

J Flood Risk Management

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This work presents an experiment involving detailed fluvial flood propagation process. Comparing to the existing flood experiments which collect hydrodynamical data just at gauges, flood evolution process in river channel and flood plain is measured and temporal–spatial data are provided. In the experiment, three inflow patterns are considered to reflect the different severity of the floods. The flood propagation and inundation are captured by using an array of surveillance cameras. The images are pre‐processed by applying camera calibration method to correct the barrel distortion. A deep learning technique is introduced to automatically identify inundated area. The inundation process is therefore obtained by identifying image series. In addition to the spatial data, the water level evolutions at three gauges are also monitored to supply detailed hydrodynamic information at gauges. The repeatability of the experiments and reliability of the deep learning technique are verified. The experimental data including spatial and point hydrodynamic features for flood events can be used to systematically validate numerical model and calibrate parameters.

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Section: Experimental Model Testsmentioning

confidence: 99%

A deep learning technique based flood propagation experiment

Hou

Bai

et al. 2021

J Flood Risk Management

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“…In other words, the amount of information of the image descriptor obtained by add method increases, but corresponding dimension doesn't increase. For example, author uses add method to cascade different region of interest (RoI) layers and then proposes a Multiple RoI (MRoI) pooling technique to extract discriminative ConvNet features efficiently [21]. The concatenate is the combination of the number of channels.…”

Section: A) Image Feature Concatenationmentioning

confidence: 99%

Adaptive Real-Time Loop Closure Detection Based on Image Feature Concatenation

Liu

Xiao

Lin

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Simultaneous Localization and Mapping (SLAM) is used to solve the problem of autonomous localization and navigation of mobile robots in unknown environments. Loop closure detection is a key part of SLAM, which largely determines accuracy and stability of SLAM. In recent years, some experiments have proved that the loop closure detection system based on neural network is superior to the traditional loop closure detection in both accuracy and real-time performance. In this paper, we propose an adaptive real-time loop closure detection (AR-Loop) method based on monocular vision. A pre-trained convolutional neural network (CNN) is used to extract image features. Then features of different layers are concatenated as image descriptors. In addition, the adaptive candidate matching range algorithm and image-tosequence calibration algorithm are proposed to improve the performance of the algorithm. Extensive experiments have been conducted on several open datasets to validate the performance of AR-Loop. It has been demonstrated that the recall rate is increased by over 18% compared with other state-of-the-art algorithms when the precision is 100%.

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“…Their localization method was compared with a manual method using the distances between automatically and manually defined reference bounding box centroids and walls. Several other works have adapted deep learning for the tasks for ROI extraction [60]- [64], and also using a high pass isotropic filter [147], and others are [146] and [124].…”

Section: A Image Preprocessing: Segmentation and Croppingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

Cited by 12 publications

References 35 publications

A deep learning technique based flood propagation experiment

A deep learning technique based flood propagation experiment

Adaptive Real-Time Loop Closure Detection Based on Image Feature Concatenation

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

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