3D LiDAR Automatic Driving Environment Detection System Based on MobileNetv3-YOLOv4

Chen, Ting-Wei; Lu, Minhua; Yan, Wei-Zhe; Fan, Yu-Cheng

doi:10.1109/icce53296.2022.9730329

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LiDAR has been widely used in many fields, such as urban planning [1], autonomous driving [2], agricultural development [3], and land use change [4]. Unlike 2D detection based on image data from optical sensors, LiDAR's spatial sensing capabilities are advantageous for object recognition in 3D spaces [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Spatial–Spectral Feature Optimization-Based Point Cloud Classification

Zhang,

Feng,

Quan

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial–spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Spatial–Spectral Feature Optimization-Based Point Cloud Classification

Zhang,

Feng,

Quan

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Convolutional-neural-network-based object detection [1] plays an important role in a wide variety of computer vision applications such as face recognition [2,3], autodriving [4,5], and public security monitoring [6]. However, the traditional CNN-based object detection method [7] contains massive parameters with huge storage and computational consumption.…”

Section: Introductionmentioning

confidence: 99%

An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection

et al. 2022

View full text Add to dashboard Cite

Object detection is a fundamental task in computer vision, which is usually based on convolutional neural networks (CNNs). While it is difficult to be deployed in embedded devices due to the huge storage and computing consumptions, binary neural networks (BNNs) can execute object detection with limited resources. However, the extreme quantification in BNN causes diversity of feature representation loss, which eventually influences the object detection performance. In this paper, we propose a method balancing Information Retention and Deviation Control to achieve effective object detection, named IR-DC Net. On the one hand, we introduce the KL-Divergence to compose multiple entropy for maximizing the available information. On the other hand, we design a lightweight convolutional module to generate scale factors dynamically for minimizing the deviation between binary and real convolution. The experiments on PASCAL VOC, COCO2014, KITTI, and VisDrone datasets show that our method improved the accuracy in comparison with previous binary neural networks.

show abstract