Developing a Driving Simulator Based Functional Object Detection Task

2012 ◽  
Vol 26 (4) ◽  
pp. 240-256
Author(s):  
Richard R. Goodenough ◽  
Johnell O. Brooks ◽  
Matthew C. Crisler ◽  
Patrick J. Rosopa
Keyword(s):  
Object Detection ◽  
Driving Simulator ◽  
Detection Task ◽  
Functional Object

Developing a New Driving Simulator Task to Assess Drivers' Functional Object Detection

2010 ◽  
Author(s):  
Richard R. Goodenough ◽  
Johnell O. Brooks ◽  
Matthew C. Crisler ◽  
William L. Logan
Keyword(s):  
Object Detection ◽  
Driving Simulator ◽  
Functional Object

Developing a New Driving Simulator Task to Assess Drivers' Functional Object Detection

2010 ◽  
Vol 54 (2) ◽  
pp. 155-159
Author(s):  
Richard R. Goodenough ◽  
Johnell O. Brooks ◽  
Matthew C. Crisler ◽  
William L. Logan
Keyword(s):  
Object Detection ◽  
Driving Simulator ◽  
Functional Object

scholarly journals One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation

2019 ◽  
Vol 10 (1) ◽  
pp. 13 ◽  
Author(s):  
Shichao Zhang ◽  
Zhe Zhang ◽  
Libo Sun ◽  
Wenhu Qin
Keyword(s):  
Object Detection ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Detection Task ◽  
Training Set ◽  
Segmentation Accuracy ◽  
Enhancement Method ◽  
Road Segmentation ◽  
Accuracy Performance

Generally, most approaches using methods such as cropping, rotating, and flipping achieve more data to train models for improving the accuracy of detection and segmentation. However, due to the difficulties of labeling such data especially semantic segmentation data, those traditional data augmentation methodologies cannot help a lot when the training set is really limited. In this paper, a model named OFA-Net (One For All Network) is proposed to combine object detection and semantic segmentation tasks. Meanwhile, using a strategy called “1-N Alternation” to train the OFA-Net model, which can make a fusion of features from detection and segmentation data. The results show that object detection data can be recruited to better the segmentation accuracy performance, and furthermore, segmentation data assist a lot to enhance the confidence of predictions for object detection. Finally, the OFA-Net model is trained without traditional data augmentation methodologies and tested on the KITTI test server. The model works well on the KITTI Road Segmentation challenge and can do a good job on the object detection task.

scholarly journals Evaluation of adaption skills in older adults using behavioural and EEG repeated measurements during a visual oddball object detection task

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Laura M. Rueda Delgado ◽  
Hugh Nolan ◽  
Alison R Buick ◽  
Florentine Barbey ◽  
John Dyer ◽  
...  
Keyword(s):  
Older Adults ◽  
Object Detection ◽  
Detection Task ◽  
Repeated Measurements ◽  
Visual Oddball

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.

Determining noise performance of co-occurrence GMuLBP on object detection task

2013 ◽  
Author(s):  
Nuh Alpaslan ◽  
Mehmet Murat Turhan ◽  
Davut Hanbay
Keyword(s):  
Object Detection ◽  
Detection Task ◽  
Noise Performance

Age Differences in Visual Abilities in Nighttime Driving Field Conditions

1996 ◽  
Vol 40 (18) ◽  
pp. 923-927 ◽  
Author(s):  
Susan T. Chrysler ◽  
Suzanne M. Danielson ◽  
Virginia M. Kirby
Keyword(s):  
Object Detection ◽  
Age Differences ◽  
Age Groups ◽  
Negative Contrast ◽  
Detection Task ◽  
Small Object ◽  
Case Scenario ◽  
Worst Case ◽  
Front Passenger ◽  
Nighttime Driving

This study was conducted to provide field data on age differences in sign legibility and object detection. Two age groups of healthy drivers with normal vision were tested for nighttime visual ability. The older group had an average age of 65.6 years and the younger group averaged 22.5 years. The field study was conducted on a private road with the subjects seated in the front passenger seat. Subjects performed a Landolt ring legibility task for four types of signs; positive and negative contrast, new and worn material. Subjects also performed object detection tasks using a small object and a pedestrian target appearing in average and low reflectance. In addition, sign legibility and object detection were completed for some trials using a simulated inclement weather visor to create a worst-case scenario. The object detection task was also completed in the presence of glare from oncoming headlamps. Results showed that older driver's legibility distances were 65% those of the younger drivers. Age differences in the object detection task ranged from a 20% to a 45% reduction for older drivers across visibility conditions.

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1925
Author(s):  
Shengzhou Xiong ◽  
Yihua Tan ◽  
Yansheng Li ◽  
Cai Wen ◽  
Pei Yan
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Task ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Attention Network ◽  
Multi Scale ◽  
Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

scholarly journals CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator

2021 ◽  
Vol 12 (1) ◽  
pp. 281
Author(s):  
Jaesung Jang ◽  
Hyeongyu Lee ◽  
Jong-Chan Kim
Keyword(s):  
Object Detection ◽  
Real World ◽  
Driving Simulator ◽  
Data Extraction ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Relevant Information ◽  
Detection Accuracy ◽  
Daunting Task ◽  
Bounding Boxes

For safe autonomous driving, deep neural network (DNN)-based perception systems play essential roles, where a vast amount of driving images should be manually collected and labeled with ground truth (GT) for training and validation purposes. After observing the manual GT generation’s high cost and unavoidable human errors, this study presents an open-source automatic GT generation tool, CarFree, based on the Carla autonomous driving simulator. By that, we aim to democratize the daunting task of (in particular) object detection dataset generation, which was only possible by big companies or institutes due to its high cost. CarFree comprises (i) a data extraction client that automatically collects relevant information from the Carla simulator’s server and (ii) a post-processing software that produces precise 2D bounding boxes of vehicles and pedestrians on the gathered driving images. Our evaluation results show that CarFree can generate a considerable amount of realistic driving images along with their GTs in a reasonable time. Moreover, using the synthesized training images with artificially made unusual weather and lighting conditions, which are difficult to obtain in real-world driving scenarios, CarFree significantly improves the object detection accuracy in the real world, particularly in the case of harsh environments. With CarFree, we expect its users to generate a variety of object detection datasets in hassle-free ways.

scholarly journals A causal relationship between face-patch activity and face-detection behavior

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Srivatsun Sadagopan ◽  
Wilbert Zarco ◽  
Winrich A Freiwald
Keyword(s):  
Object Detection ◽  
Causal Relationship ◽  
Face Detection ◽  
Real World ◽  
Face Processing ◽  
Detection Task ◽  
Natural Scenes ◽  
Critical Importance ◽  
Primate Brain

The primate brain contains distinct areas densely populated by face-selective neurons. One of these, face-patch ML, contains neurons selective for contrast relationships between face parts. Such contrast-relationships can serve as powerful heuristics for face detection. However, it is unknown whether neurons with such selectivity actually support face-detection behavior. Here, we devised a naturalistic face-detection task and combined it with fMRI-guided pharmacological inactivation of ML to test whether ML is of critical importance for real-world face detection. We found that inactivation of ML impairs face detection. The effect was anatomically specific, as inactivation of areas outside ML did not affect face detection, and it was categorically specific, as inactivation of ML impaired face detection while sparing body and object detection. These results establish that ML function is crucial for detection of faces in natural scenes, performing a critical first step on which other face processing operations can build.

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