A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

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Robust object proposals re-ranking for object detection in autonomous driving using convolutional neural networks

Signal Processing Image Communication ◽

10.1016/j.image.2017.02.007 ◽

2017 ◽

Vol 53 ◽

pp. 110-122 ◽

Cited By ~ 31

Author(s):

Cuong Cao Pham ◽

Jae Wook Jeon

Keyword(s):

Neural Networks ◽

Object Detection ◽

Convolutional Neural Networks ◽

Autonomous Driving ◽

Object Proposals

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Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

Computational Intelligence and Neuroscience ◽

10.1155/2019/9142753 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 6

Author(s):

Qimei Wang ◽

Feng Qi ◽

Minghe Sun ◽

Jianhua Qu ◽

Jie Xue

Keyword(s):

Neural Networks ◽

Object Detection ◽

Convolutional Neural Networks ◽

Detection Methods ◽

Disease Detection ◽

Deep Convolutional Neural Networks ◽

Detection Techniques ◽

Tomato Diseases ◽

Validation Set ◽

Tomato Disease

This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.

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