An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time

Vijayakumar, V.; Garg, Dweepna; Kotecha, Ketan

doi:10.3390/fi13120307

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…The FLOPS is related to the device’s energy consumption (the higher the FLOPS, the higher the energy consumption). The floating-point operation numbers are computed as follows [ 63 ]: For a convolution layer with n filters of size

applied to

feature maps (W: width; H: height; C: channels), with P: number of parameters:

For a max-pooling layer or an upsampling layer with a window of size

feature maps ( W : width; H : height; C : channels):

…”

Section: Resultsmentioning

confidence: 99%

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CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

Ndayikengurukiye

Mignotte

2023

Sensors

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Salient object-detection models attempt to mimic the human visual system’s ability to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently achieved high performance. However, developing deep neural network models with the same performance for resource-limited vision sensors or mobile devices remains a challenge. In this work, we propose CoSOV1net, a novel lightweight salient object-detection neural network model, inspired by the cone- and spatial-opponent processes of the primary visual cortex (V1), which inextricably link color and shape in human color perception. Our proposed model is trained from scratch, without using backbones from image classification or other tasks. Experiments on the most widely used and challenging datasets for salient object detection show that CoSOV1Net achieves competitive performance (i.e., Fβ=0.931 on the ECSSD dataset) with state-of-the-art salient object-detection models while having a low number of parameters (1.14 M), low FLOPS (1.4 G) and high FPS (211.2) on GPU (Nvidia GeForce RTX 3090 Ti) compared to the state of the art in lightweight or nonlightweight salient object-detection tasks. Thus, CoSOV1net has turned out to be a lightweight salient object-detection model that can be adapted to mobile environments and resource-constrained devices.

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applied to

feature maps (W: width; H: height; C: channels), with P: number of parameters:

For a max-pooling layer or an upsampling layer with a window of size

feature maps ( W : width; H : height; C : channels):

…”

Section: Resultsmentioning

confidence: 99%

“…The FLOPS is related to the device's energy consumption (the higher the FLOPS, the higher the energy consumption). The floating-point operation numbers are computed as follows [63]:…”

Section: Lightweight Measuresmentioning

confidence: 99%

CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

Ndayikengurukiye

Mignotte

2023

Sensors

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show abstract

“…Object detection models could fail to detect when facing the varying sizes of the objects that have low resolution. RPN in the Mask R-CNN uses multi-scale anchor box to enhance the detection accuracy by extracting features at the multiple convolution levels of the object [ 37 , 38 ]. The ROI head generated mask predictions, classifications, and bounding box predictions.…”

Section: Materials and Methodsmentioning

confidence: 99%

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

et al. 2022

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An analysis of scar tissue is necessary to understand the pathological tissue conditions during or after the wound healing process. Hematoxylin and eosin (HE) staining has conventionally been applied to understand the morphology of scar tissue. However, the scar lesions cannot be analyzed from a whole slide image. The current study aimed to develop a method for the rapid and automatic characterization of scar lesions in HE-stained scar tissues using a supervised and unsupervised learning algorithm. The supervised learning used a Mask region-based convolutional neural network (RCNN) to train a pattern from a data representation using MMDetection tools. The K-means algorithm characterized the HE-stained tissue and extracted the main features, such as the collagen density and directional variance of the collagen. The Mask RCNN model effectively predicted scar images using various backbone networks (e.g., ResNet50, ResNet101, ResNeSt50, and ResNeSt101) with high accuracy. The K-means clustering method successfully characterized the HE-stained tissue by separating the main features in terms of the collagen fiber and dermal mature components, namely, the glands, hair follicles, and nuclei. A quantitative analysis of the scar tissue in terms of the collagen density and directional variance of the collagen confirmed 50% differences between the normal and scar tissues. The proposed methods were utilized to characterize the pathological features of scar tissue for an objective histological analysis. The trained model is time-efficient when used for detection in place of a manual analysis. Machine learning-assisted analysis is expected to aid in understanding scar conditions, and to help establish an optimal treatment plan.

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“…Multi-scale feature fusion network is widely used in computer vision tasks such as target detection and image classification. Varadarajan et al. (2021) designed an object detection network composed of 22 convolution layers.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-scale feature fusion network is widely used in computer vision tasks such as target detection and image classification. Varadarajan et al (2021) designed an object detection network composed of 22 convolution layers. By using multi-scale feature fusion technology, the network can well identify objects of different sizes and shapes from images.…”

Section: Introductionmentioning

confidence: 99%

Crop classification in high-resolution remote sensing images based on multi-scale feature fusion semantic segmentation model

Gao

Wang

2023

Front. Plant Sci.

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The great success of deep learning in the field of computer vision provides a development opportunity for intelligent information extraction of remote sensing images. In the field of agriculture, a large number of deep convolutional neural networks have been applied to crop spatial distribution recognition. In this paper, crop mapping is defined as a semantic segmentation problem, and a multi-scale feature fusion semantic segmentation model MSSNet is proposed for crop recognition, aiming at the key problem that multi-scale neural networks can learn multiple features under different sensitivity fields to improve classification accuracy and fine-grained image classification. Firstly, the network uses multi-branch asymmetric convolution and dilated convolution. Each branch concatenates conventional convolution with convolution nuclei of different sizes with dilated convolution with different expansion coefficients. Then, the features extracted from each branch are spliced to achieve multi-scale feature fusion. Finally, a skip connection is used to combine low-level features from the shallow network with abstract features from the deep network to further enrich the semantic information. In the experiment of crop classification using Sentinel-2 remote sensing image, it was found that the method made full use of spectral and spatial characteristics of crop, achieved good recognition effect. The output crop classification mapping was better in plot segmentation and edge characterization of ground objects. This study can provide a good reference for high-precision crop mapping and field plot extraction, and at the same time, avoid excessive data acquisition and processing.

show abstract

An Efficient Deep Convolutional Neural Network Approach for Object Detection and Recognition Using a Multi-Scale Anchor Box in Real-Time

Cited by 12 publications

References 18 publications

CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

Automated Structural Analysis and Quantitative Characterization of Scar Tissue Using Machine Learning

Crop classification in high-resolution remote sensing images based on multi-scale feature fusion semantic segmentation model

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