A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot

Ji, Wei; Qian, Zhijie; Xu, Bo; Zhao, Debin

doi:10.1177/1729881417753871

Cited by 26 publications

(15 citation statements)

References 17 publications

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“…Moreover, Liu et al [143] combined the Retinex algorithm with bilateral filtering, thereby effectively improving the color distortion and detail loss in the final image but also increasing the complexity of the algorithm [144], [145]. Yin et al [146], Mulyantini and Choi [147], Zhang et al [148], Ji et al [149], and Zhang et al [150] proposed Retinex-based algorithms combined with guided filters [151]. Particularly during the early stage of research on Retinex algorithms, scholars obtained many fruitful findings.…”

Section: ) Other Retinex Algorithmsmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: ) Other Retinex Algorithmsmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…Data set preprocessing is an important part of deep learning. The filter function of the single-scale retinex [23] is modified to solve the problem of high-intensity light in this paper. The high-pass filter is used to instead of the original Gaussian low-pass filter to obtain a low-pass output image, which reduces the reflection of the image.…”

Section: A Retinex With Low-pass Outputmentioning

confidence: 99%

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

et al. 2020

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Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS). INDEX TERMS Northern maize leaf blight, disease detection, transmission module, retinex, single shot multiBox detector (SSD). I. INTRODUCTION Maize is one of the major food crops in the world. The planting area and output of maize in the world are only lower than that of wheat and rice [1]. In addition to be an excellent feed for animal husbandry, maize is also an important raw material for the development of light industrial products. The associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.

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“…However, during the segmentation process, there will be noise in the image. According to the basic morphological characteristics of green pepper, the segmented image is opened and closed by using a 5×5 element matrix to remove part of the noise [17]. It can be seen from the figure that the segmentation result is well, reaching the expectation, which is convenient for the next step of recognition processing.…”

Section: Image Segmentationmentioning

confidence: 99%

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Chen

et al. 2019

IEEE Access

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In the green pepper harvesting robot, the color of green pepper is similar to that of leaves, which makes it difficult to recognize the green pepper target. In order to solve this problem, a green pepper recognition method based on least-squares support vector machine optimized by the improved particle swarm optimization (IPSO-LSSVM) is proposed in this paper. Firstly, the green pepper images are segmented by K-Means method under the Lab color space, and the segmentation images of the target and background are obtained. The processed green pepper image was divided into training and testing samples. Then, the shape and texture features of green pepper targets are extracted separately from the training sample using the hu invariant moment and Tamura texture feature. Meanwhile, in order to reduce the complexity of data calculations and improve the efficiency, the extracted feature vectors are normalized. The feature vector is used as the input eigenvector of the least-squares support vector machine (LSSVM). The particle swarm optimization algorithm is used to obtain the optimal regularization parameter and the kernel function width. In order to maintain the particle activity, the mutation strategy is introduced to improve the particle swarm optimization algorithm. The experimental results show that the recognition rate of IPSO-LSSVM is higher than that of other methods, and the recognition accuracy is 89.04%. It could meet the requirements of green pepper identification.

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A nighttime image enhancement method based on Retinex and guided filter for object recognition of apple harvesting robot

Cited by 26 publications

References 17 publications

An Experiment-Based Review of Low-Light Image Enhancement Methods

An Experiment-Based Review of Low-Light Image Enhancement Methods

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

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