A survey of 3D object detection algorithms for intelligent vehicles development

Li, Zhen; Du, Yixuan; Zhu, Miaomiao; Zhou, Shi Sheng; Zhang, Lifeng

doi:10.1007/s10015-021-00711-0

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Transformer can exhibit formidable modeling capabilities and parallel computing prowess. Li et al (2023) conducted a comprehensive review of Transformer-based target detection algorithms, categorizing them into four aspects: feature learning, target estimation, label matching strategy and algorithm application. A comparative analysis was performed between Transformer-based algorithms and convolutional neural network (CNN) algorithms in target detection tasks.…”

Section: Related Workmentioning

confidence: 99%

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

Wang,

Wu,

Zheng

2023

Front. Plant Sci.

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IntroductionAccurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed.MethodsThe TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network.ResultThe results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model’s performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results ( R2 =0.89), with the root mean square error of 1.54.DiscussionThe TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades.

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Section: Related Workmentioning

confidence: 99%

TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection

Wang,

Wu,

Zheng

2023

Front. Plant Sci.

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“…Only by augmenting the 2D image feature or designing an efficient algorithm, the available feature can be refined. To solve the problem of severely lacking depth information compared to LiDAR-based methods [1][2][3][4][12][13] or other camera-based methods [5][6][7][8][9][10][11], geometry prior is recently being used together.…”

Section: Monocular-based 3d Object Detectionmentioning

confidence: 99%

“…Many existing 3D Object detection used KITTI Datasets [13] as training datasets. The KITTI dataset is a collection of data with two RGB cameras, so only the Monocular and Stereo methods were possible.…”

Section: Multi-view 3d Object Detectionmentioning

confidence: 99%

“…Survey work for various approaches of 3D object detection has been published several times before [12][13]. However, existing survey works [12][13] have mainly explained the difference between the modality methods, such as camera based, point cloud, and sensor fusion. In this paper, we will make survey of the latest models for 3D perception and classification with only camera image.…”

Section: Introductionmentioning

confidence: 99%

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A Survey for 3D Object Detection Algorithms from Images

Lee¹,

Kim²,

Kim³

2022

J Multimed Inf Syst

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Image-based 3D object detection is one of the important and difficult problems in autonomous driving and robotics, and aims to find and represent the location, dimension and orientation of the object of interest. It generates three dimensional (3D) bounding boxes with only 2D images obtained from cameras, so there is no need for devices that provide accurate depth information such as LiDAR or Radar. Image-based methods can be divided into three main categories: monocular, stereo, and multi-view 3D object detection. In this paper, we investigate the recent state-of-the-art models of the above three categories. In the multi-view 3D object detection, which appeared together with the release of the new benchmark datasets, NuScenes and Waymo, we discuss the differences from the existing monocular and stereo methods. Also, we analyze their performance and discuss the advantages and disadvantages of them. Finally, we conclude the remaining challenges and a future direction in this field.

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“…In order to deal with this problem, some works estimate the distance of each pixel [70,71], creating a depth map from the monocular camera. However, the most common approach is to use a different sensor to sense the objects in 3D and obtain the depth of the obstacles detected from the images [72]. The sensors that fill this gap are stereo cameras and LiDAR sensors.…”

Section: Perception Technologiesmentioning

confidence: 99%