RSANet: Towards Real-Time Object Detection with Residual Semantic-Guided Attention Feature Pyramid Network

Zhou, Quan; Wang, Jie; Liu, Jia; Li, Sheng‐Hua; Ou, Weihua; Jin, Xin

doi:10.1007/s11036-020-01723-z

Cited by 20 publications

(21 citation statements)

References 35 publications

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“…Maintaining the same level of abstraction, but extending the analysis on Table 1 to the columns that contribute with specific data regarding each of the components that comprise the architecture of the different detection systems considered, we see that there is a marginal number of papers, namely MAOD [92], CornerNet-Squeeze [93], and LightDet [94], that explore the joint application of adjustments on backbone, neck, and head. The remaining majority is evenly split between work that explores enhancements on two of the elements that form the detection system in its different permutations [91,[95][96][97][98][99][100][101][102][103][104][105][106], and approaches that choose to focus on just one component [48,[107][108][109][110][111][112][113][114][115][116][117]. The main object of interest in the latter case is the neck, and, to a lesser extent, the backbone.…”

Section: Lightweight Object Detection Frameworkmentioning

confidence: 99%

“…Apart from two specific contributions that propose efforts directly related to the exploitation of multiscale features-increasing the number of different scale levels considered for the output [98] and using encoder-decoder structures for feature generation at different levels [109]we identify table methods in the lightweight-detection-architecture-devoted that are essentially located in the space of solutions aimed at obtaining more valuable features, semantically speaking. More specifically, data presented in the table in this respect create a scenario where the fusion of multiscale feature maps [94] constitutes the dominant approach and where related works primarily focus both on different information transfer and exchange structures, namely dense connections [103,115] and inverted residual blocks [99, 104,105], and on attention mechanisms, an approach primarily aimed at extracting more discriminative features, mainly channel-wise [98,104,105] but also simultaneously at the spatial and channel level [102]. Additionally, several other approaches that also seek to improve the network's expressiveness can be identified, but, in this case, they are achieved by enriching intermediate features due to the use of the convolution on dimensionreduction blocks [97] or by using attention modules to adjust the feature distribution and thus facilitate the distinction between background and front features (spatial attention) [91].…”

Section: Classification According To the Enhancement Type Producedmentioning

confidence: 99%

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Optimized convolutional neural network architectures for efficient on-device vision-based object detection

Rodriguez-Conde

Campos

Fdez-Riverola

2021

Neural Comput & Applic

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Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off.

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Section: Lightweight Object Detection Frameworkmentioning

confidence: 99%

Section: Classification According To the Enhancement Type Producedmentioning

confidence: 99%

Optimized convolutional neural network architectures for efficient on-device vision-based object detection

Rodriguez-Conde

Campos

Fdez-Riverola

2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…In many applications, object detection devices are a crucial component. Object detection has been widely used for applications and systems of environmental reasoning [1][2][3][4][5]. Therefore, rigorous research in this field is always active and essential.…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Efficient Network for Accurate Object Detection

Guan¹,

Aamir²,

Hu³

et al. 2021

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Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance, image object detection was treated as a twofold problem, involving object proposal generation and object classification. First, a framework was designed to generate high-quality, class-independent, accurate proposals. Then, these proposals, together with their input images, were imported to our network to learn convolutional features. To boost detection efficiency, the number of proposals was reduced by a network refinement module, leaving only a few eligible candidate proposals. After that, the refined candidate proposals were loaded into the detection module to classify the objects. The proposed model was tested on the test set of the famous PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results clearly demonstrate that our model achieved robust overall detection efficiency over existing approaches using fewer or more proposals, in terms of recall, mean average best overlap (MABO), and mean average precision (mAP).

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“…It has become a hot issue in the field of vehicle target detection. In response to the above problems, researchers have proposed feature extraction methods such as feature pyramids [3][4][5] and target detection algorithms without anchor frame [6,7]. Some scholars [8] proposed a spatiotemporal event interaction model (STEIM) on this basis to solve the problem of time and data interaction in the V2X environment.…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Detection Model Based on 5G‐V2X for Smart City Security Perception

Liu

et al. 2021

Wireless Communications and Mobile Computing

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Security perception systems based on 5G-V2X have become an indispensable part of smart city construction. However, the detection speed of traditional deep learning models is slow, and the low-latency characteristics of 5G networks cannot be fully utilized. In order to improve the safety perception ability based on 5G-V2X, increase the detection speed in vehicle perception. A vehicle perception model is proposed. First, an adaptive feature extraction method is adopted to enhance the expression of small-scale features and improve the feature extraction ability of small-scale targets. Then, by improving the feature fusion method, the shallow information is fused layer by layer to solve the problem of feature loss. Finally, the attention enhancement method is introduced to increase the center point prediction ability and solve the problem of target occlusion. The experimental results show that the UA-DETRAC data set has a good detection effect. Compared with the vehicle detection capability before the improvement, the detection accuracy and speed have been greatly improved, which effectively improves the security perception capability based on the 5G-V2X system, thereby promoting the construction of smart cities.

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RSANet: Towards Real-Time Object Detection with Residual Semantic-Guided Attention Feature Pyramid Network

Cited by 20 publications

References 35 publications

Optimized convolutional neural network architectures for efficient on-device vision-based object detection

Optimized convolutional neural network architectures for efficient on-device vision-based object detection

A Region-Based Efficient Network for Accurate Object Detection

A Vehicle Detection Model Based on 5G‐V2X for Smart City Security Perception

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