机器人视觉系统中的物体检测技术研究
发布时间：2018-09-08 20:22
【摘要】：物体检测技术是协助机器人视觉系统感知、定位视野中存在的客观物体的关键技术,是机器人视觉系统完成场景理解等高层视觉任务的基础。研究并提升机器人视觉系统中的物体检测算法的性能对提升机器人的智能程度具有重要意义。本文将机器人视觉系统中的物体检测问题根据目标物体的泛化程度划分为三个层次:刚性物体检测、习得物体检测和任意物体检测。然后遵循从简单到复杂、从具体到泛化的顺序对这三类检测问题依次展开研究。针对刚性物体检测,本文结合所在课题组开发的两种机器人(火电厂用温度探头组装机器人和中国象棋对弈机器人)对物体检测的实际工程需求,提出了两种刚性物体检测算法。该两种算法已在各自的机械臂手眼系统中顺利运行。然后,本文对习得物体检测进行探究,尤其关注卷积神经网络在习得物体检测中的应用。针对具有广泛应用前景的自然场景下的二代身份证检测问题,本文提出了一种可用于端到端的习得物体检测的卷积神经网络模型。在自然场景图片中的二代身份证检测任务中,该模型在本文收集的数据集上取得了 79.51%的平均覆盖率。最后,本文对任意物体检测问题展开研究并创新地提出了基于图像分割结构化特征的任意物体检测算法。该算法在候选集容量为1000时在权威数据集PASCAL VOC2007上取得了 96.1%的检测率。在候选集容量小于100时,检测性能优于四种性能突出的经典算法。
[Abstract]:Object detection technology is the key technology to assist the robot vision system to perceive and locate the objective objects in the visual field. It is also the foundation of the robot vision system to complete the high-level vision tasks such as scene understanding. It is very important to study and improve the performance of object detection algorithm in robot vision system. In this paper, the object detection problem in robot vision system is divided into three levels according to the generalization of target object: rigid object detection, acquisition object detection and arbitrary object detection. Then follow the order from simplicity to complexity and from concrete to generalization to study these three detection problems in turn. Aiming at the rigid object detection, this paper combines the actual engineering requirements of two kinds of robots (temperature probe assembled robot and Chinese chess game robot) developed by our research group for object detection. Two kinds of rigid object detection algorithms are proposed. The two algorithms have been running smoothly in the hand-eye system of each manipulator. Then, this paper explores acquisition object detection, especially the application of convolution neural network in acquisition object detection. Aiming at the problem of second-generation ID card detection in natural scenes with wide application prospects, a convolution neural network model for end-to-end acquisition object detection is proposed in this paper. In the second generation ID card detection task in natural scene images, the model achieves an average coverage of 79.51% on the data set collected in this paper. Finally, this paper studies the problem of arbitrary object detection and proposes an algorithm of arbitrary object detection based on the structured feature of image segmentation. The algorithm achieves a detection rate of 96.1% on the authoritative dataset PASCAL VOC2007 when the candidate set capacity is 1000. When the capacity of candidate set is less than 100, the detection performance is better than four classical algorithms with outstanding performance.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP242
本文编号：2231596
[Abstract]:Object detection technology is the key technology to assist the robot vision system to perceive and locate the objective objects in the visual field. It is also the foundation of the robot vision system to complete the high-level vision tasks such as scene understanding. It is very important to study and improve the performance of object detection algorithm in robot vision system. In this paper, the object detection problem in robot vision system is divided into three levels according to the generalization of target object: rigid object detection, acquisition object detection and arbitrary object detection. Then follow the order from simplicity to complexity and from concrete to generalization to study these three detection problems in turn. Aiming at the rigid object detection, this paper combines the actual engineering requirements of two kinds of robots (temperature probe assembled robot and Chinese chess game robot) developed by our research group for object detection. Two kinds of rigid object detection algorithms are proposed. The two algorithms have been running smoothly in the hand-eye system of each manipulator. Then, this paper explores acquisition object detection, especially the application of convolution neural network in acquisition object detection. Aiming at the problem of second-generation ID card detection in natural scenes with wide application prospects, a convolution neural network model for end-to-end acquisition object detection is proposed in this paper. In the second generation ID card detection task in natural scene images, the model achieves an average coverage of 79.51% on the data set collected in this paper. Finally, this paper studies the problem of arbitrary object detection and proposes an algorithm of arbitrary object detection based on the structured feature of image segmentation. The algorithm achieves a detection rate of 96.1% on the authoritative dataset PASCAL VOC2007 when the candidate set capacity is 1000. When the capacity of candidate set is less than 100, the detection performance is better than four classical algorithms with outstanding performance.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP242
【参考文献】
相关期刊论文 前1条
1 王殿君;;基于视觉的中国象棋棋子识别定位技术[J];清华大学学报(自然科学版);2013年08期
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