scholarly journals Improved Object Proposals with Geometrical Features for Autonomous Driving

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Yiliu Feng ◽  
Wanzeng Cai ◽  
Xiaolong Liu ◽  
Huini Fu ◽  
Yafei Liu ◽  
...  
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Bayesian Framework ◽  
Neural Net ◽  
High Quality ◽  
Geometrical Features ◽  
Object Proposals ◽  
General Object

This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.

scholarly journals Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

2021 ◽  
Vol 13 (24) ◽  
pp. 5065
Author(s):  
Bei Cheng ◽  
Zhengzhou Li ◽  
Hui Li ◽  
Zhiquan Ding ◽  
Tianqi Qin
Keyword(s):  
Object Detection ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Autonomous Learning ◽  
Region Merging ◽  
Residual Network ◽  
High Quality ◽  
Association Coefficient ◽  
Image Object Detection ◽  
Image Object

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods.

scholarly journals HCNET: A Point Cloud Object Detection Network Based on Height and Channel Attention

2021 ◽  
Vol 13 (24) ◽  
pp. 5071
Author(s):  
Jing Zhang ◽  
Jiajun Wang ◽  
Da Xu ◽  
Yunsong Li
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Attention Mechanism ◽  
Uneven Distribution ◽  
Adaptive Adjustment ◽  
High Level

The use of LiDAR point clouds for accurate three-dimensional perception is crucial for realizing high-level autonomous driving systems. Upon considering the drawbacks of the current point cloud object-detection algorithms, this paper proposes HCNet, an algorithm that combines an attention mechanism with adaptive adjustment, starting from feature fusion and overcoming the sparse and uneven distribution of point clouds. Inspired by the basic idea of an attention mechanism, a feature-fusion structure HC module with height attention and channel attention, weighted in parallel, is proposed to perform feature-fusion on multiple pseudo images. The use of several weighting mechanisms enhances the ability of feature-information expression. Additionally, we designed an adaptively adjusted detection head that also overcomes the sparsity of the point cloud from the perspective of original information fusion. It reduces the interference caused by the uneven distribution of the point cloud from the perspective of adaptive adjustment. The results show that our HCNet has better accuracy than other one-stage-network or even two-stage-network RCNNs under some evaluation detection metrics. Additionally, it has a detection rate of 30FPS. Especially for hard samples, the algorithm in this paper has better detection performance than many existing algorithms.

scholarly journals Visible-Thermal Image Object Detection via the Combination of Illumination Conditions and Temperature Information

2021 ◽  
Vol 13 (18) ◽  
pp. 3656
Author(s):  
Hang Zhou ◽  
Min Sun ◽  
Xiang Ren ◽  
Xiuyuan Wang
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
A Priori ◽  
Autonomous Driving ◽  
Data Sources ◽  
A Priori Knowledge ◽  
Detection Accuracy ◽  
Thermal Images ◽  
Deep Learning Network ◽  
Priori Knowledge

Object detection plays an important role in autonomous driving, disaster rescue, robot navigation, intelligent video surveillance, and many other fields. Nonetheless, visible images are poor under weak illumination conditions, and thermal infrared images are noisy and have low resolution. Consequently, neither of these two data sources yields satisfactory results when used alone. While some scholars have combined visible and thermal images for object detection, most did not consider the illumination conditions and the different contributions of diverse data sources to the results. In addition, few studies have made use of the temperature characteristics of thermal images. Therefore, in the present study, visible and thermal images are utilized as the dataset, and RetinaNet is used as the baseline to fuse features from different data sources for object detection. Moreover, a dynamic weight fusion method, which is based on channel attention according to different illumination conditions, is used in the fusion component, and the channel attention and a priori temperature mask (CAPTM) module is proposed; the CAPTM can be applied to a deep learning network as a priori knowledge and maximizes the advantage of temperature information from thermal images. The main innovations of the present research include the following: (1) the consideration of different illumination conditions and the use of different fusion parameters for different conditions in the feature fusion of visible and thermal images; (2) the dynamic fusion of different data sources in the feature fusion of visible and thermal images; (3) the use of temperature information as a priori knowledge (CAPTM) in feature extraction. To a certain extent, the proposed methods improve the accuracy of object detection at night or under other weak illumination conditions and with a single data source. Compared with the state-of-the-art (SOTA) method, the proposed method is found to achieve superior detection accuracy with an overall mean average precision (mAP) improvement of 0.69%, including an AP improvement of 2.55% for the detection of the Person category. The results demonstrate the effectiveness of the research methods for object detection, especially temperature information-rich object detection.

scholarly journals Unsupervised Subcategory Domain Adaptive Network for 3D Object Detection in LiDAR

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 927
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Liang Xiao ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Adaptive Method ◽  
Target Domain ◽  
Adaptive Network ◽  
General Object ◽  
Dimensional Object ◽  
3D Object Detection

Three-dimensional object detection based on the LiDAR point cloud plays an important role in autonomous driving. The point cloud distribution of the object varies greatly at different distances, observation angles, and occlusion levels. Besides, different types of LiDARs have different settings of projection angles, thus producing an entirely different point cloud distribution. Pre-trained models on the dataset with annotations may degrade on other datasets. In this paper, we propose a method for object detection using an unsupervised adaptive network, which does not require additional annotation data of the target domain. Our object detection adaptive network consists of a general object detection network, a global feature adaptation network, and a special subcategory instance adaptation network. We divide the source domain data into different subcategories and use a multi-label discriminator to assign labels dynamically to the target domain data. We evaluated our approach on the KITTI object benchmark and proved that the proposed unsupervised adaptive method could achieve a remarkable improvement in the adaptation capabilities.

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 Detection of Schools in Remote Sensing Images Based on Attention-Guided Dense Network

2021 ◽  
Vol 10 (11) ◽  
pp. 736
Author(s):  
Han Fu ◽  
Xiangtao Fan ◽  
Zhenzhen Yan ◽  
Xiaoping Du
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Dense Network ◽  
Remote Sensing Images ◽  
Composite Object ◽  
Detection Algorithms

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.

scholarly journals A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Wang ◽  
Ziyue Wang ◽  
Zhengwei Xu ◽  
Chi Wang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Speed ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Time Operation ◽  
On The Road

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

scholarly journals An Object Detection Framework Based on Deep Features and High-Quality Object Locations

2021 ◽  
Vol 38 (3) ◽  
pp. 719-730
Author(s):  
Yurong Guan ◽  
Muhammad Aamir ◽  
Zhihua Hu ◽  
Zaheer Ahmed Dayo ◽  
Ziaur Rahman ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Efficiency ◽  
High Quality ◽  
Softmax Classifier ◽  
Body Of Knowledge ◽  
Object Locations ◽  
Object Proposal ◽  
Object Proposals ◽  
Traditional Approaches ◽  
Image Object Detection

Objection detection has long been a fundamental issue in computer vision. Despite being widely studied, it remains a challenging task in the current body of knowledge. Many researchers are eager to develop a more robust and efficient mechanism for object detection. In the extant literature, promising results are achieved by many novel approaches of object detection and classification. However, there is ample room to further enhance the detection efficiency. Therefore, this paper proposes an image object detection and classification, using a deep neural network (DNN) for based on high-quality object locations. The proposed method firstly derives high-quality class-independent object proposals (locations) through computing multiple hierarchical segments with super pixels. Next, the proposals were ranked by region score, i.e., several contours wholly enclosed in the proposed region. After that, the top-ranking object proposal was adopted for post-classification by the DNN. During the post-classification, the network extracts the eigenvectors from the proposals, and then maps the features with the softmax classifier, thereby determining the class of each object. The proposed method was found superior to traditional approaches through an evaluation on Pascal VOC 2007 Dataset.

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