Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEfficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks
211
0
0.0
(
0
)
Added by
Marwan Dhuheir
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their outstanding ability to be used in different sectors and serve in difficult and dangerous areas. Moreover, the advancements in computer vision and artificial intelligence have increased the use of UAVs in various applications and solutions, such as forest fires detection and borders monitoring. However, using deep neural networks (DNNs) with UAVs introduces several challenges of processing deeper networks and complex models, which restricts their on-board computation. In this work, we present a strategy aiming at distributing inference requests to a swarm of resource-constrained UAVs that classifies captured images on-board and finds the minimum decision-making latency. We formulate the model as an optimization problem that minimizes the latency between acquiring images and making the final decisions. The formulated optimization solution is an NP-hard problem. Hence it is not adequate for online resource allocation. Therefore, we introduce an online heuristic solution, namely DistInference, to find the layers placement strategy that gives the best latency among the available UAVs. The proposed approach is general enough to be used for different low decision-latency applications as well as for all CNN types organized into the pipeline of layers (e.g., VGG) or based on residual blocks (e.g., ResNet).
rate research
Read More
Deep Convolutional Neural Networks (DCNNs) are capable of obtaining powerful image representations, which have attracted great attentions in image recognition. However, they are limited in modeling orientation transformation by the internal mechanism. In this paper, we develop Orientation Convolution Networks (OCNs) for image recognition based on the proposed Landmark Gabor Filters (LGFs) that the robustness of the learned representation against changed of orientation can be enhanced. By modulating the convolutional filter with LGFs, OCNs can be compatible with any existing deep learning networks. LGFs act as a Gabor filter bank achieved by selecting $ p $ $ left( ll nright) $ representative Gabor filters as andmarks and express the original Gabor filters as sparse linear combinations of these landmarks. Specifically, based on a matrix factorization framework, a flexible integration for the local and the global structure of original Gabor filters by sparsity and low-rank constraints is utilized. With the propogation of the low-rank structure, the corresponding sparsity for representation of original Gabor filter bank can be significantly promoted. Experimental results over several benchmarks demonstrate that our method is less sensitive to the orientation and produce higher performance both in accuracy and cost, compared with the existing state-of-art methods. Besides, our OCNs have few parameters to learn and can significantly reduce the complexity of training network.
Single-image-based view generation (SIVG) is important for producing 3D stereoscopic content. Here, handling different spatial resolutions as input and optimizing both reconstruction accuracy and processing speed is desirable. Latest approaches are based on convolutional neural network (CNN), and they generate promising results. However, their use of fully connected layers as well as pre-trained VGG forces a compromise between reconstruction accuracy and processing speed. In addition, this approach is limited to the use of a specific spatial resolution. To remedy these problems, we propose exploiting fully convolutional networks (FCN) for SIVG. We present two FCN architectures for SIVG. The first one is based on combination of an FCN and a view-rendering network called DeepView$_{ren}$. The second one consists of decoupled networks for luminance and chrominance signals, denoted by DeepView$_{dec}$. To train our solutions we present a large dataset of 2M stereoscopic images. Results show that both of our architectures improve accuracy and speed over the state of the art. DeepView$_{ren}$ generates competitive accuracy to the state of the art, however, with the fastest processing speed of all. That is x5 times faster speed and x24 times lower memory consumption compared to the state of the art. DeepView$_{dec}$ has much higher accuracy, but with x2.5 times faster speed and x12 times lower memory consumption. We evaluated our approach with both objective and subjective studies.
We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat.
Inspired by the conclusion that humans choose the visual cortex regions corresponding to the real size of an object to analyze its features when identifying objects in the real world, this paper presents a framework, SizeNet, which is based on both the real sizes and features of objects to solve object recognition problems. SizeNet was used for object recognition experiments on the homemade Rsize dataset, and was compared with the state-of-the-art methods AlexNet, VGG-16, Inception V3, Resnet-18, and DenseNet-121. The results showed that SizeNet provides much higher accuracy rates for object recognition than the other algorithms. SizeNet can solve the two problems of correctly recognizing objects with highly similar features but real sizes that are obviously different from each other, and correctly distinguishing a target object from interference objects whose real sizes are obviously different from the target object. This is because SizeNet recognizes objects based not only on their features, but also on their real size. The real size of an object can help exclude the interference objects categories whose real size ranges do not match the real size of the object, which greatly reduces the objects categories number in the label set used for the downstream object recognition based on object features. SizeNet is of great significance for studying the interpretable computer vision. Our code and dataset will thus be made public.
While the depth of convolutional neural networks has attracted substantial attention in the deep learning research, the width of these networks has recently received greater interest. The width of networks, defined as the size of the receptive fields and the density of the channels, has demonstrated crucial importance in low-level vision tasks such as image denoising and restoration. However, the limited generalization ability, due to the increased width of networks, creates a bottleneck in designing wider networks. In this paper, we propose the Deep Regulated Convolutional Network (RC-Net), a deep network composed of regulated sub-network blocks cascaded by skip-connections, to overcome this bottleneck. Specifically, the Regulated Convolution block (RC-block), featured by a combination of large and small convolution filters, balances the effectiveness of prominent feature extraction and the generalization ability of the network. RC-Nets have several compelling advantages: they embrace diversified features through large-small filter combinations, alleviate the hazy boundary and blurred details in image denoising and super-resolution problems, and stabilize the learning process. Our proposed RC-Nets outperform state-of-the-art approaches with significant performance gains in various image restoration tasks while demonstrating promising generalization ability. The code is available at https://github.com/cswin/RC-Nets.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
