Deep Learning in Object Detection

Pang, Yanwei; Cao, Jiale

doi:10.1007/978-981-10-5152-4_2

Cited by 15 publications

(13 citation statements)

References 54 publications

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“…Finally, more filters are added to each convolutional layer to extract a more comprehensive set of features, which in turn improves classification accuracy. [15] VGGNet contains many levels of networks; the more commonly used ones are VGGNet-16 (see Fig. 4) and VGGNet-19.…”

Section: Methodsmentioning

confidence: 99%

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network

Zhong

Qin

et al. 2023

Medicine

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When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. Objective: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. Methods: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. Results: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. Conclusion: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability.

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Section: Methodsmentioning

confidence: 99%

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network

Zhong

Qin

et al. 2023

Medicine

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“…It uses multiscale prediction to detect the final target, and its results are more accurate than YOLO [20][21][22]. With each training batch, YOLOv5 passes training data through a data loader [23]. Moreover, the best anchor box in different datasets can be adaptively calculated [24].…”

Section: B Improved Yolov5mentioning

confidence: 99%

An Underwater Target Wake Detection in Multi-Source Images Based on Improved YOLOv5

Shi

2023

IEEE Access

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In the marine environment, underwater targets improve "stealth" performance through innovation in technology, so traditional target tracking methods are difficult to use for tracking underwater targets stably and accurately. This paper proposes an improved YOLOv5 (You Look Only Once) based method to detect wake of underwater target accurately in multi-source images. We obtain optical images and thermal images through model simulation and scaled experiment. Improve the generalization ability of the detection network through data augmentation. The proposed method introduces linear features detection to enhance feature propagation, and promote feature reuse. The result shows that the proposed method is better than the original object detection algorithms. The improved algorithm can detect underwater targets status (underwater or surface) and image types (optical wake or thermal wake).

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“…For the second aspect, it was possible to find different deep learning approaches used on object detection problems. For example, Pang and Cao in [10] analyzed some object detection methods based on deep learning. They compare typical CNN-based architectures focused on a specific use case, pedestrian detection.…”

Section: Related Workmentioning

confidence: 99%

Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features

Gómez¹,

Genevois²,

Lussereau³

et al. 2021

Preprint

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Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deeplearning methodologies allowed the development of object detection approaches with better performance. However, there is still the challenge to obtain more characteristics from the objects detected in real-time. The main reason is that more information from the environment's objects can improve the autonomous vehicle capacity to face different urban situations. This paper proposes a new approach to detect static and dynamic objects in front of an autonomous vehicle. Our approach can also get other characteristics from the objects detected, like their position, velocity, and heading. We develop our proposal fusing results of the environment's interpretations achieved of YoloV3 and a Bayesian filter. To demonstrate our proposal's performance, we asses it through a benchmark dataset and real-world data obtained from an autonomous platform. We compared the results achieved with another approach.

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Deep Learning in Object Detection

Cited by 15 publications

References 54 publications

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network

Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network

An Underwater Target Wake Detection in Multi-Source Images Based on Improved YOLOv5

Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features

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