As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024 512 input image, and both precision and efficiency have been improved over combination of single tasks.
The ImageNet Large Scale Visual Recognition Challenge @cite_2 provides enough labelled image databases for robust image classification over a wide range of object categories. Convolutional neural network models that perform image classification tasks on the ImageNet dataset have been a common choice in recent years: AlexNet @cite_5 , ZFNet @cite_16 , VGGNet @cite_23 , Residual Network @cite_0 GoogLeNet @cite_13 , Inception V3 @cite_15 , Xception @cite_22 and MobileNet @cite_9 .
@cite_22 @cite_9 @cite_0 @cite_23 @cite_2 @cite_5 @cite_15 @cite_16 @cite_13
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We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55 using no external data which is over a 20 relative improvement on the previous year's winner. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the 5 years of the challenge, and propose future directions and improvements. The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. In the last few years, Convolutional Neural Networks (CNNs) had become the default paradigm to address classification problems, specially, but not only, in image recognition. This is mainly due to the high success rate that they provide. Despite there currently exist approaches that apply deep learning to the 3D recognition problem, they are either too slow for online uses or too error prone. To fill this gap, we propose LonchaNet, a deep learning architecture for point clouds classification. Our system successfully achieves a high accuracy yet providing a low computation cost. A dense set of experiments were carried out in order to validate our system in the frame of the ModelNet — a large-scale 3D CAD models dataset — challenge. Our proposal achieves a success rate of 94.37 in the ModelNet-10 classification task, the second place in the leaderboard as of today (November, 2016).