Deep Neural Network Pruning and Sensor Fusion in Practical 2D Detection

Abstract

Convolutional Neural Networks (CNNs) have been extensively studied and applied to various computer vision problems, including object detection, semantic segmentation, and autonomous driving. Convolutional Neural Networks (CNN)s extract complex features from input images or data to represent objects or patterns. Their highly complex architecture, however, and the size of their learned weights make their time and resource intensive. Measures like pruning and fusion, which aim to simplify the structure and lessen the load on the network’s resources, should be considered to resolve this problem. In this thesis, we intend to explore the effect of pruning on segmentation and object detection as well as the benefits of using sensor fusion operators in the 2d space to boost the existing networks’ performance. Specifically, we focus on structured pruning, quantization, and simple and learnable fusion operators. We also study the scalability of different algorithms in terms of the number of parameters and floating points used. First, we provide a general overview of CNNs and the history of pruning and fusion operations. Second, we explain the advantages of pruning and discuss the contrast between the unstructured and structured types. Third, we discuss the differences between simple fusion and learnable fusion. In order to evaluate our algorithms, we use several classification and object detection datasets such as Cifar-10, KITTI and Microsoft COCO. By applying our proposed methods to the studied datasets, we can assess the efficiency of the algorithms. Furthermore, this allows us to observe the improvements in task-specific losses. In conclusion, our work is focused on analyzing the effect of pruning and fusion to simplify existing networks and improve their performance in terms of scalability, task-specific losses, and resource consumption. We also discuss various algorithms, as well as datasets which serve as a basis for the evaluation of our proposed approaches.