基于点云特征增强的复杂室内场景3D目标检测

doi:10.11996/JG.j.2095-302X.2025010059

图学学报 ›› 2025, Vol. 46 ›› Issue (1): 59-69.DOI: 10.11996/JG.j.2095-302X.2025010059

• 图像处理与计算机视觉 • 上一篇下一篇

基于点云特征增强的复杂室内场景3D目标检测

苑朝(), 赵明雪, 张丰羿, 冯晓勇, 李冰, 陈瑞()

华北电力大学自动化系，河北保定 071003

收稿日期:2024-08-02 接受日期:2024-09-23 出版日期:2025-02-28 发布日期:2025-02-14
通讯作者:陈瑞(1993-)，女，讲师，博士。主要研究方向为电力视觉导航和多模态目标检测等。E-mail：chenrui_cr@ncepu.edu.cn
第一作者:苑朝(1985-)，男，副教授，博士。主要研究方向为计算机视觉和多轴机械臂智能控制等。E-mail：chaoyuan@ncepu.edu.cn
基金资助:
国家自然科学基金联合基金重点支持项目(U21A20486);中央高校基本科研业务费专项资金(2024MS137)

Point cloud feature enhanced 3D object detection in complex indoor scenes

YUAN Chao(), ZHAO Mingxue, ZHANG Fengyi, FENG Xiaoyong, LI Bing, CHEN Rui()

Department of Automation, North China Electric Power University, Baoding Hebei 071003, China

Received:2024-08-02 Accepted:2024-09-23 Published:2025-02-28 Online:2025-02-14
Contact: CHEN Rui (1993-), lecturer, Ph.D. Her main research interests cover power vision navigation and multimodal object detection, etc. E-mail：chenrui_cr@ncepu.edu.cn
First author：YUAN Chao (1985-), associate professor, Ph.D. His main research interests cover computer vision and intelligent control of multi-axis manipulator, etc. E-mail：chaoyuan@ncepu.edu.cn
Supported by:
The National Natural Science Foundation of China Joint Fund of China is a Key Support Project(U21A20486);Funded by the Central University Basic Research Business Fund(2024MS137)

摘要/Abstract

摘要：

在复杂室内场景下的3D点云目标检测中，点云规模大且目标密集细节多。针对现有检测算法处理点云数据时会丢失大量局部特征且不能提取足够的空间信息与语义信息，致使检测精度低的问题，提出了一种基于改进VoteNet的点云特征增强的复杂室内场景3D目标检测(PFE)算法。首先，利用动态特征补偿模块模拟种子点集与分组集点云特征的交互查询过程，逐步恢复丢失的特征来进行特征补偿；其次，在特征提取部分引入残差MLP模块，通过残差结构搭建更深层的特征学习网络以挖掘更细节的点云特征；最后，在目标提案生成阶段引入特征自注意力机制对一组独立的目标点进行语义关系建模，生成新的特征映射。在公开数据集SUN RGB-D和ScanNet V2上进行实验，实验证明改进后的模型对室内目标的检测精度相较于基准模型在mAP@0.25上分别提升了5.0%和11.5%，大量的消融实验证明了每个改进模块的有效性。

关键词: 室内场景, 三维点云, 目标检测, 特征补偿, 交互查询, 残差, 自注意力机制, 特征映射

Abstract:

3D point cloud object detection in complex indoor scenes presents challenges due to large-scale point clouds and dense objects with many details. When dealing with point cloud data, existing detection algorithms lose a significant amount of local features and fail to extract enough spatial and semantic information, resulting in low detection accuracy. To solve this problem, a point cloud features enhanced 3D object detection in complex indoor scenes (PEF) algorithm was proposed based on an improved VoteNet. Firstly, a dynamic feature compensation module was used to simulate the interactive query process between seed point set features and grouping set features, gradually recovering lost features for feature compensation. Secondly, a residual MLP module was introduced into the feature extraction part, and a deeper feature learning network was constructed through a residual structure to mine more detailed point cloud features. Finally, in the proposal stage, a feature self-attention mechanism was introduced to model the semantic relationship between a set of independent object points, generating a new feature map. Experiments conducted on the public datasets SUN RGB-D and ScanNet V2 demonstrated that the improved model enhanced the detection accuracy for indoor objects by 5.0% and 11.5% respectively on mAP@0.25 compared with the baseline model. Extensive ablation experiments confirmed the effectiveness of each improved module.

Key words: indoor scene, 3D point cloud, object detection, feature compensation, interactive query, residual, self-attention, feature map

中图分类号:

TP391.41

苑朝, 赵明雪, 张丰羿, 冯晓勇, 李冰, 陈瑞. 基于点云特征增强的复杂室内场景3D目标检测[J]. 图学学报, 2025, 46(1): 59-69.

YUAN Chao, ZHAO Mingxue, ZHANG Fengyi, FENG Xiaoyong, LI Bing, CHEN Rui. Point cloud feature enhanced 3D object detection in complex indoor scenes[J]. Journal of Graphics, 2025, 46(1): 59-69.

图/表 14

图1 PFE检测方法整体框架

Fig. 1 Overall framework of PFE object detection method

图2 PointNet++特征提取网络结构

Fig. 2 PointNet++ feature extraction network structure

图3 动态特征补偿模块网络结构

Fig. 3 Dynamic feature compensation module network structure

图4 残差MLP模块

Fig. 4 Residual MLP blocks

图5 特征注意力机制模块

Fig. 5 Feature self-attention block

表1 在SUN RGB-D验证集与ScanNet V2验证集上与其他先进3D目标检测算法的性能比较

Table 1 Performance comparison with SOTA 3D object detection networks on SUN RGB-D validation set and ScanNet V2 validation set

Method	Input	SUN RGB-D		ScanNet V2
Method	Input	mAP @0.25	mAP @0.50	mAP @0.25	mAP @0.50
DSS^[34]	Geo+RGB	42.1	-	15.2	6.8
2D-driven^[35]	Geo+RGB	45.1	-	-	-
F-PointNet^[36]	Geo+RGB	54.0	-	19.8	10.8
HGNet^[37]	Geo only	61.6	-	61.3	34.4
RGNet^[38]	Geo only	59.2	-	48.5	26.0
MLCVNet^[15]	Geo only	59.8	-	64.7	42.1
3DETR^[39]	Geo only	59.1	32.7	65.0	47.0
Pointformer^[18]	Geo only	61.1	-	64.1	42.6
BRNet^[40]	Geo only	61.1	43.7	66.1	50.9
H3DNet^[41]	Geo only	60.1	39.0	67.2	48.1
PBNet^[42]	Geo only	-	-	69.3	60.1
VoteNet^[13]	Geo only	57.7	32.9	58.6	33.5
Ours	Geo only	62.7	39.3	70.1	50.9

表2 本方法与其他方法在ScanNet V2验证集上的比较(评估指标是IOU阈值为0.25的平均精度)

Table 2 Results comparison with other methods on the ScanNet V2 validation set (The evaluation metric is the average precision with 0.25 IOU threshold)

Method	Cab	Bed	Chair	Sofa	Table	Door	Wind	Bkshf	Pic	Cntr
VoteNet^[13]	38.10	87.92	56.13	89.62	58.77	57.13	37.20	54.70	7.83	88.71
MLCVNet^[15]	42.45	88.84	89.98	87.40	63.50	56.93	46.98	56.94	11.94	63.94
Pointformer^[18]	46.70	88.40	90.50	88.70	65.70	55.00	47.70	55.80	18.00	63.80
H3DNet^[41]	49.40	88.60	91.80	90.20	64.90	61.00	51.90	54.90	18.60	62.00
Group-free^[43]	52.10	91.90	93.60	88.00	70.70	60.70	53.70	62.40	16.10	58.50
Ours	52.44	92.06	94.13	92.11	65.31	63.77	53.76	61.45	33.89	62.10
Method	Desk	Curt	Fridg	Showr	Toil	Sink	Bath	Gabgb	mAP
VoteNet^[13]	71.69	47.23	45.37	47.32	94.94	44.62	92.11	36.27	58.65
MLCVNet^[15]	76.05	56.72	60.86	65.91	98.33	59.18	87.22	47.89	64.48
Pointformer^[18]	69.10	55.40	48.50	66.20	98.90	61.50	86.70	47.40	64.10
H3DNet^[41]	75.90	57.30	57.20	75.30	97.90	67.40	92.50	53.60	67.20
Group-free^[43]	80.90	67.90	47.00	76.30	99.60	72.00	95.30	56.40	69.10
Ours	79.63	56.83	62.06	82.74	99.85	62.78	90.01	56.89	70.10

表3 不同模块的消融实验

Table 3 Ablation studies about different modules

Method	DFC	RMLP	FSA	SUN RGB-D		ScanNet V2
Method	DFC	RMLP	FSA	mAP @0.25	mAP @0.50	mAP @0.25	mAP @0.50
Baseline	-	-	-	57.7	32.9	58.6	33.5
A	√	-	-	60.1	37.3	66.6	46.9
B	√	√	-	61.3	37.8	68.4	48.0
C	-	-	√	58.2	33.7	62.4	39.9
D	√	√	√	62.7	39.3	70.1	50.9

图6 方法A在SUN RGB-D与ScanNet V2验证集上的可视化结果((a) SUN RGB-D验证集；(b) ScanNet V2验证集)

Fig. 6 The visual results of the method A on the SUN RGB-D and ScanNet V2 validation set ((a) SUN RGB-D validation set; (b) ScanNet V2 validation set)

表4 DFC模块在ScanNet V2验证集上的性能

Table 4 Performance of the DFC block on the ScanNet V2 validation set

Method	Cab	Bed	Chair	Sofa	Table	Door	Wind	Bkshf	Pic	Cntr
Baseline	38.10	87.92	56.13	89.62	58.77	57.13	37.20	54.70	7.83	88.71
A	52.35	89.37	89.68	90.39	62.23	56.26	51.58	56.37	30.58	53.84
Method	Desk	Curt	Fridg	Showr	Toil	Sink	Bath	Gabgb	mAP
Baseline	71.69	47.23	45.37	47.32	94.94	44.62	92.11	36.27	58.65
A	72.73	56.14	56.65	80.34	98.6	60.63	91.48	49.48	66.59

表5 RMLP模块层数消融实验

Table 5 Ablation studies about the number of RMLP module layers

Layer	ScanNet V2
Layer	mAP@0.25	mAP@0.50
1	67.2	47.5
2	68.4	48.0
3	67.8	47.2

图7 方法B在SUN RGB-D与ScanNet V2验证集上的可视化结果((a) SUN RGB-D验证集；(b) ScanNet V2验证集)

Fig. 7 The visual results of the method B on the SUN RGB-D and ScanNet V2 validation set ((a) SUN RGB-D validation set; (b) ScanNet V2 validation set)

图8 方法C在SUN RGB-D与ScanNet V2验证集上的可视化结果((a) SUN RGB-D验证集；(b) ScanNet V2验证集)

Fig. 8 The visual results of the method C on the SUN RGB-D and ScanNet V2 validation set ((a) SUN RGB-D validation set; (b) ScanNet V2 validation set)

图9 SUN RGB-D验证集上消融实验的可视化结果((a)原图；(b)真值；(c)基准模型；(d)模型A；(e)模型B；(f)模型D)

Fig. 9 The visual results of ablation studies on the SUN RGB-D validation set ((a) Original drawing; (b) Ground truth; (c) The baseline model; (d) Model A; (e) Model B; (f) Model D)

参考文献 43

[1]	MCCORMAC J, CLARK R, BLOESCH M, et al. Fusion++: volumetric object-level SLAM[C]// 2018 International Conference on 3D Vision. New York: IEEE Press, 2018: 32-41.
[2]	WANG C, XU D F, ZHU Y K, et al. DenseFusion: 6D object pose estimation by iterative dense fusion[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 3338-3347.
[3]	PARK Y, LEPETIT V, WOO W. Multiple 3D object tracking for augmented reality[C]// 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality. New York: IEEE Press, 2008: 117-120.
[4]	张旭, 王智, 崔粲, 等. 二维激光雷达定位建图系统设计与实现[J]. 光学技术, 2019, 45(5): 596-600.
	ZHANG X, WANG Z, CUI C, et al. Design and implementation of 2D lidar positioning and mapping system[J]. Optical Technique, 2019, 45(5): 596-600 (in Chinese).
[5]	ZHOU Y, TUZEL O. VoxeLNet: end-to-end learning for point cloud based 3d object detection[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 4490-4499.
[6]	LANG A H, VORA S, CAESAR H, et al. PointPillars: fast encoders for object detection from point clouds[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 12689-12697.
[7]	HE C H, ZENG H, HUANG J Q, et al. Structure aware single-stage 3D object detection from point cloud[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2020: 11870-11879.
[8]	YE M S, XU S J, CAO T Y. HVNet: hybrid voxel network for lidar based 3d object detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2020: 1628-1637.
[9]	QI C R, SU H, KAICHUN M, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2017: 77-85.
[10]	QI C R, LI Y, HAO S, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]// The 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 5105-5114.
[11]	YANG Z T, SUN Y A, LIU S, et al. 3DSSD: point-based 3D single stage object detector[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2020: 11037-11045.
[12]	SHI S S, WANG X G, LI H S. PointrCNN: 3D object proposal generation and detection from point cloud[C]// Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 770-779.
[13]	QI C R, OR L, HE K M, et al. Deep Hough voting for 3d object detection in point clouds[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2019: 9276-9285.
[14]	周锐闯, 田瑾, 闫丰亭, 等. 融合外部注意力和图卷积的点云分类模型[J]. 图学学报, 2023, 44(6): 1162-1172. DOI
	ZHOU R C, TIAN J, YAN F T, et al. Point cloud classification model incorporating external attention and graph convolution[J]. Journal of Graphics, 2023, 44(6): 1162-1172 (in Chinese).
[15]	XIE Q, LAI Y K, WU J, et al. MLCVNet: multi-level context VoteNet for 3D object detection[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2020: 10444-10453.
[16]	NOH J, LEE S, HAM B. HVPR: hybrid voxel-point representation for single-stage 3D object detection[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 14600-14609.
[17]	NIE J H, HE Z W, YANG Y X, et al. GLT-T: global-local transformer voting for 3D single object tracking in point clouds[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Washington DC: PKP Press, 2023: 1957-1965.
[18]	PAN X R, XIA Z F, SONG S J, et al. 3D object detection with Pointformer[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 7459-7468.
[19]	周静, 胡怡宇, 黄心汉. 形状补全引导的Transformer点云目标检测方法[J]. 智能系统学报, 2023, 18(4): 731-742.
	ZHOU J, HU Y Y, HUANG X H. Shape completion-guided Transformer point cloud object detection method[J]. CAAI Transactions on Intelligent Systems, 2023, 18(4): 731-742 (in Chinese).
[20]	XU M T, DING R Y, ZHAO H S, et al. PAConv: position adaptive convolution with dynamic kernel assembling on point clouds[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 3172-3181.
[21]	LIU Y C, FAN B, XIANG S M, et al. Relation-shape convolutional neural network for point cloud analysis[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2019: 8887-8896.
[22]	THOMAS H, QI C R, DESCHAUD J E, et al. KPConv: flexible and deformable convolution for point clouds[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2019: 6410-6419.
[23]	MA X, QIN C, YOU H, et al. Rethinking network design and local geometry in point cloud: a simple residual MLP framework[EB/OL]. [2024-04-15]. https://arxiv.org/abs/2202.07123?context=cs.
[24]	梁奥, 李峙含, 花海洋. PointMLP-FD: 基于多级自适应下采样的点云分类模型[J]. 图学学报, 2023, 44(1): 112-119. DOI
	LIANG A, LI Z H, HUA H Y. PointMLP-FD: a point cloud classification model based on multi-level adaptive downsampling[J]. Journal of Graphics, 2023, 44(1): 112-119 (in Chinese). DOI
[25]	杨积升, 章云, 李东. 点云目标检测残差投票网络[J]. 广东工业大学学报, 2022, 39(1): 56-62.
	YANG J S, ZHANG Y, LI D. A residual neural network with voting for 3D object detection in point clouds[J]. Journal of Guangdong University of Technology, 2022, 39(1): 56-62 (in Chinese).
[26]	董雯, 林靖宇. 基于语义特征的3D点云室内目标检测[J]. 计算机与数字工程, 2023, 51(6): 1285-1290.
	DONG W, LIN J Y. 3D point cloud indoor object detection based on semantic features[J]. Computer & Digital Engineering, 2023, 51(6): 1285-1290 (in Chinese).
[27]	王婧暄. 面向机器人自动焊接的工件检测与扫描路径规划研究[D]. 哈尔滨: 哈尔滨工业大学, 2023.
	WANG J X. Research on workpiece detection and scan path planning for robot automated welding[D]. Harbin:Harbin Institute of Technology, 2023 (in Chinese).
[28]	李奇. 复杂室内场景三维目标文本描述方法研究[D]. 西安: 西安电子科技大学, 2022.
	LI Q. Research on 3D object caption generation in complex indoor environment[D]. Xi'an: Xidian University, 2022 (in Chinese).
[29]	LIANG Y, FU Y. CascadeV-Det: cascade point voting for 3D object detection[EB/OL]. [2024-07-17]. https://arxiv.org/abs/2401.07477.
[30]	HOU H R, FING M T, WU Z J, et al. 3D object detection from point cloud via voting step diffusion[EB/OL]. [2024-06-02]. https://ieeexplore.ieee.org/document/10602535.
[31]	张新良, 付陈琳, 赵运基. 扩展点态卷积网络的点云分类分割模型[J]. 中国图象图形学报, 2020, 25(8): 1551-1557.
	ZHANG X L, FU C L, ZHAO Y J. Extended pointwise convolution network model for point cloud classification and segmentation[J]. Journal of Image and Graphics, 2020, 25(8): 1551-1557 (in Chinese).
[32]	杨军, 党吉圣. 基于上下文注意力CNN的三维点云语义分割[J]. 通信学报, 2020, 41(7): 195-203. DOI
	YANG J, DANG J S. Semantic segmentation of 3D point cloud based on contextual attention CNN[J]. Journal on Communications, 2020, 41(7): 195-203 (in Chinese). DOI
[33]	DAI A, CHANG A X, SAVVA M, et al. ScanNet: richly-annotated 3D reconstructions of indoor scenes[C]// 2017 IEEE CVFCVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2017: 2432-2443.
[34]	SONG S R, XIAO J X. Deep sliding shapes for amodal 3D object detection in RGB-D images[C]// 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2016: 808-816.
[35]	LAHOUD J, GHANEM B. 2D-driven 3d object detection in RGB-D images[C]// 2017 IEEE International Conference on Computer Vision. New York: IEEE Press, 2017: 4632-4640.
[36]	QI C R, LIU W, WU C, et al. Frustum PointNets for 3D object detection from RGB-D data[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 918-927.
[37]	CHEN J T, LEI B W, SONG Q Y, et al. A hierarchical graph network for 3D object detection on point clouds[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2020: 389-398.
[38]	FENG M T, GILANI S Z, WANG Y N, et al. Relation graph network for 3d object detection in point clouds[J]. IEEE Transactions on Image Processing, 2021, 30: 92-107. DOI PMID
[39]	WANG Y, GUIZILINI V C, ZHANG T, et al. DETR3D: 3D object detection from multi-view images via 3D-to-2D queries[EB/OL]. [2024-06-02]. https://proceedings.mlr.press/v164/wang22b.html.
[40]	CHENG B W, SHENG L, SHI S S, et al. Back-tracing representative points for voting-based 3D object detection in point clouds[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2021: 8959-8968.
[41]	ZHANG Z W, SUN B, YANG H T, et al. H3DNET: 3D object detection using hybrid geometric primitives[C]// The 16th European Conference on Computer Vision. Cham: Springer, 2020: 311-329.
[42]	ZHAO W G, YAN Y Y, YANG C L, et al. Divide and conquer: 3D point cloud instance segmentation with point-wise binarization[C]// 2023 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2023: 562-571.
[43]	LIU Z, ZHANG Z, CAO Y, et al. Group-free 3D object detection via transformers[C]// 2021 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2021: 2929-2938.

基于点云特征增强的复杂室内场景3D目标检测

Point cloud feature enhanced 3D object detection in complex indoor scenes

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 43

相关文章 15

编辑推荐

Metrics

本文评价

[1]	程旭东, 史彩娟, 高炜翔, 王森, 段昌钰, 闫晓东. 面向域自适应目标检测的一致无偏教师模型[J]. 图学学报, 2025, 46(1): 114-125.
[2]	崔克彬, 耿佳昌. 基于EE-YOLOv8s的多场景火灾迹象检测算法[J]. 图学学报, 2025, 46(1): 13-27.
[3]	陈冠豪, 徐丹, 贺康建, 施洪贞, 张浩. 基于转置注意力和CNN的图像超分辨率重建网络[J]. 图学学报, 2025, 46(1): 35-46.
[4]	王杨, 马唱, 胡明, 孙涛, 饶元, 袁振羽. 基于多尺度特征融合的轻量型野外蝙蝠检测[J]. 图学学报, 2025, 46(1): 70-80.
[5]	孙前来, 林绍杭, 刘东峰, 宋晓阳, 刘佳耀, 刘瑞珍. 基于元学习的小样本指针式仪表检测方法[J]. 图学学报, 2025, 46(1): 81-93.
[6]	李琼, 考月英, 张莹, 徐沛. 面向无人机航拍图像的目标检测研究综述[J]. 图学学报, 2024, 45(6): 1145-1164.
[7]	李珍峰, 符世琛, 徐乐, 孟博, 张昕, 秦建军. 基于MBI-YOLOv8的煤矸石目标检测算法研究[J]. 图学学报, 2024, 45(6): 1301-1312.
[8]	闫建红, 冉同霄. 基于YOLOv8的轻量化无人机图像目标检测算法[J]. 图学学报, 2024, 45(6): 1328-1337.
[9]	姜晓恒, 段金忠, 卢洋, 崔丽莎, 徐明亮. 融合先验知识推理的表面缺陷检测[J]. 图学学报, 2024, 45(5): 957-967.
[10]	章东平, 魏杨悦, 何数技, 徐云超, 胡海苗, 黄文君. 特征融合与层间传递：一种基于Anchor DETR改进的目标检测方法[J]. 图学学报, 2024, 45(5): 968-978.
[11]	彭文, 林金炜. 基于空间信息关注和纹理增强的短小染色体分类方法[J]. 图学学报, 2024, 45(5): 1017-1029.
[12]	李建华, 韩宇, 石开铭, 张可嘉, 郭红领, 方东平, 曹佳明. 施工现场小目标工人检测方法[J]. 图学学报, 2024, 45(5): 1040-1049.
[13]	孙己龙, 刘勇, 周黎伟, 路鑫, 侯小龙, 王亚琼, 王志丰. 基于DCNv2和Transformer Decoder的隧道衬砌裂缝高效检测模型研究[J]. 图学学报, 2024, 45(5): 1050-1061.
[14]	刘宗明, 洪唯, 龙睿, 祝越, 张小宇. 基于自注意机制的乳源瑶绣自动生成与应用研究[J]. 图学学报, 2024, 45(5): 1096-1105.
[15]	李大湘, 吉展, 刘颖, 唐垚. 改进YOLOv7遥感图像目标检测算法[J]. 图学学报, 2024, 45(4): 650-658.