High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

Ren, Jie; Guan, Fuyu; Wang, Tingting; Qian, Baoshan; Chengji, Luo; Cai, Guoliang; Kan, Ce; Li, Xiaofeng

doi:10.1155/2022/6596868

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…For height positioning model, there are systematic and computational model errors when estimating the height of clustered pod-peppers combined with depth information In case of our depth camera D435i, they include calibration error, 0-2% recording error for depth image, image distortion due to camera jitter, and estimation error incurred from formula (9). Scholars have attempted multiple highprecision-camera assisted shooting [26] and high-precision calibration algorithm [27] to minimize three-dimensional positioning errors.…”

Section: Discussionmentioning

confidence: 99%

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Zhang

Chen

2022

IEEE Access

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Traditionally height of end effector of pod pepper harvester is fixed, which induces it hardly adapt to growth height of clustered peppers. Firstly, aiming at the problems of small size and clustered growth of pepper fruits during identification task, an improved Faster R-CNN algorithm is proposed. On the one hand, strategies such as increasing the types and number of high-resolution anchors and using RoI Align instead of RoI Pooling are employed to improve the detection accuracy for tiny targets. On the other hand, ResNet+FPN instead of VGG16 and ResNet backbone structure is adopted as the low-level feature extractor, so extracting capability for small features can be enhanced effectively. Furthermore, to precisely locate the position of clustered peppers, a height calculation model combining the 2D image recognition results with its depth information is advanced. Comparative experiments show that the overall accuracy AP and AP50 of our method reach 75.79% and 87.30%, respectively. Compared with VGG16 feature extraction model, the two indicators are improved by 8.7% and 1.3%, respectively. The small target detection accuracy AP small is increased about 11.4%, with recall rate AR small increased up to 10.2%. The overall loss rate Loss is reduced by 4.7%, which manifests greatly improvement compared to YOLOv3 model. The detection time of a single frame reaches 42ms, which is slightly longer than that of YOLOv3 network, but it can still meet the real-time detection requirements of pepper harvester. In 3D location experiment, the average absolute height error of clustered peppers from the ground is 4.4mm, that accounts to the relative average error of 1.1%, thus suffices the adjustment error requirement of the end effector.

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Section: Discussionmentioning

confidence: 99%

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Zhang

Chen

2022

IEEE Access

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“…By capturing two images of an object from different positions, binocular cameras can determine the 3D geometric information of the object by calculating the position deviation between the corresponding points of the images [28]. This process is based on the principle of parallax, and it enables the camera to accurately measure the distance to the object, providing important information for crop row detection [29]. Stereo vision-based crop row detection has demonstrated superior performance in challenging field conditions such as high crop density or varying lighting [30].…”

Section: Binocular Camerasmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

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“…Binocular stereo vision is a visual localization algorithm based on parallax, which calculates the specific position of the target point through trigonometric, geometric relationships. The different parts of the camera can be divided into two types: a simple model and a general model [16][17][18][19][20][21].…”

Section: Binocular Stereo Vision 3d Modementioning

confidence: 99%

Analysis of binocular visual perception technology of underwater robot

Zhuang¹,

Zhao²,

Wang³

et al. 2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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Vision technology plays an important role when AUVs (Autonomous Underwater Vehicles) operate underwater. In this paper, the three-dimensional mode of binocular stereo vision is constructed to complete the positioning of the target. This article then explains the problem of distorted underwater images and introduces a method to correct distorted images. Based on underwater physical imaging, the underwater image processing methods are divided into underwater image enhancement and underwater image restoration. The research status of the two ways is analyzed and reviewed. The advantages and disadvantages of the above methods are summarized and discussed, and the future development trend is predicted.

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High Precision Calibration Algorithm for Binocular Stereo Vision Camera using Deep Reinforcement Learning

Cited by 11 publications

References 19 publications

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Identification and Depth Localization of Clustered Pod Pepper Based on Improved Faster R-CNN

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

Analysis of binocular visual perception technology of underwater robot

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