Wheat Head Detection Method Based on Image Processing and Deep Learning

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

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Study of filming condition for deep learning based crack detection method

10.5592/co/cetra.2020.1059 ◽

2021 ◽

Author(s):

Tomotaka Fukuoka ◽

Takahiro Minami ◽

Makoto Fujiu ◽

Junichi Takayama

Keyword(s):

Image Processing ◽

Deep Learning ◽

Service Life ◽

Visual Inspection ◽

Crack Detection ◽

Detection Method ◽

Processing Method ◽

Inspection Method ◽

Image Processing Method

Recently, the study of extending the service life of bridges has gained attention. In Japan, there are about 730,000 bridges with a length of 2 m or longer, and many of these were built during a period of high economic growth, and have now reached the end of their service life. Therefore, their rebuilding and the extension of their service life must be considered. However, some local public organizations have problems that insufficient manpower relative to the number of bridges to manage, as well as insufficient funding for maintenance. Thus, these organizations are unable to perform routine close visual inspections. Specific problems include “notably less staff and consulting technicians relative to the number of bridges to be managed” and “high inspection cost preventing from funding for repair.” As issues with the continuing close visual inspection of bridges are surfacing, the remote imaging system is expected to become a new inspection method that replaces close visual inspection. The practical potential of bridge inspections using images captured with a super-high-resolution camera was examined. A super-high-resolution camera enables us to take a wide area picture of a target bridge from a long distance. An image processing method could improve the efficiency of image-based inspection method. For example, a deep learning-based image processing method could extract a damaged area on a surface of a bridge automatically with high accuracy faster than human inspection. In general, the accuracy of an image processing method is affected by the quality of an input image. Filming conditions are one of the factors that determine the quality of a photo image. It is important to evaluate the effect of filming conditions to improve the reliability of an image processing method. In this paper, we evaluate the effect of the filming conditions for an image processing method by comparing the results of a deep learning-based crack detection method.

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