FM-Mnet Real-Time Object Detection Model for Video Feature Fusion Processing

2021 ◽

Vol 9 (VIII) ◽

pp. 857-864

Author(s):

Vibhavari B Rao

Keyword(s):

Deep Learning ◽

Object Detection ◽

Computer Architecture ◽

Real Time ◽

Surveillance System ◽

Crime Scene ◽

Detection Model ◽

Detection Algorithms ◽

Performance Metric ◽

Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

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Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Remote Sensing ◽

10.3390/rs12010182 ◽

2020 ◽

Vol 12 (1) ◽

pp. 182 ◽

Cited By ~ 10

Author(s):

Lingxuan Meng ◽

Zhixing Peng ◽

Ji Zhou ◽

Jirong Zhang ◽

Zhenyu Lu ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Detection System ◽

Detection Model ◽

Aerial Vehicle ◽

Real Time Detection ◽

Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

Remote Sensing ◽

10.3390/rs13020160 ◽

2021 ◽

Vol 13 (2) ◽

pp. 160

Author(s):

Jiangqiao Yan ◽

Liangjin Zhao ◽

Wenhui Diao ◽

Hongqi Wang ◽

Xian Sun

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Feature Fusion ◽

Detection Algorithm ◽

Detection Performance ◽

Natural Image ◽

Detection Model ◽

Multi Scale ◽

Feature Pyramid ◽

Scale Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.

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SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

Applied Sciences ◽

10.3390/app11031096 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1096

Author(s):

Qing Li ◽

Yingcheng Lin ◽

Wei He

Keyword(s):

Object Detection ◽

Real Time ◽

Large Scale ◽

Feature Fusion ◽

Contextual Information ◽

Attention Mechanism ◽

Detection Accuracy ◽

Single Shot ◽

Feature Maps ◽

Embedded Devices

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.

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Dynamic Object Detection Algorithm Based on Lightweight Shared Feature Pyramid

Remote Sensing ◽

10.3390/rs13224610 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4610

Author(s):

Li Zhu ◽

Zihao Xie ◽

Jing Luo ◽

Yuhang Qi ◽

Liman Liu ◽

...

Keyword(s):

Object Detection ◽

Feature Fusion ◽

Computational Cost ◽

Detection Algorithm ◽

Dynamic Object ◽

Shared Feature ◽

Detection Model ◽

Detection Algorithms ◽

Adaptive Inference ◽

Feature Pyramid

Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a lightweight shared feature pyramid is proposed, which performs adaptive inference according to computing resources and the difficulty of samples, greatly improving the efficiency of inference. Specifically, a lightweight shared feature pyramid network and lightweight detection head is proposed to reduce the amount of computation and parameters in the feature fusion part and detection head of the dynamic object detection model. On the PASCAL VOC dataset, under the two conditions of “anytime prediction” and “budgeted batch object detection”, the performance, computation amount and parameter amount are better than the dynamic object detection models constructed by networks such as ResNet, DenseNet and MSDNet.

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Efficient Object Detection Model for Real-time UAV Application

Computer and Information Science ◽

10.5539/cis.v14n1p45 ◽

2021 ◽

Vol 14 (1) ◽

pp. 45

Author(s):

Subrahmanyam Vaddi ◽

Dongyoun Kim ◽

Chandan Kumar ◽

Shafqat Shad ◽

Ali Jannesari

Keyword(s):

Object Detection ◽

Real Time ◽

High Performance ◽

Class Imbalance ◽

Detection Techniques ◽

Detection Model ◽

Deep Feature ◽

Proposed Model ◽

Negative Effect ◽

Feature Pyramid

Unmanned Aerial Vehicles (UAVs) equipped with vision capabilities have become popular in recent years. Many applications have especially been employed object detection techniques extracted from the information captured by an onboard camera. However, object detection on UAVs requires high performance, which has a negative effect on the result. In this article, we propose a deep feature pyramid architecture with a modified focal loss function, which enables it to reduce the class imbalance. Moreover, the proposed method employed an end to end object detection model running on the UAV platform for real-time application. To evaluate the proposed architecture, we combined our model with Resnet and MobileNet as a backend network, and we compared it with RetinaNet and HAL-RetinaNet. Our model produced a performance of 30.6 mAP with an inference time of 14 fps. This result shows that our proposed model outperformed RetinaNet by 6.2 mAP.

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Real-Time Object Detection Model

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst061267 ◽

2020 ◽

Vol 6 (12) ◽

pp. 360-364

Author(s):

Akash Kumar, Dr. Amita Goel Prof. Vasudha Bahl and Prof. Nidhi Sengar

Keyword(s):

Neural Network ◽

Computer Vision ◽

Object Detection ◽

Real Time ◽

Real World ◽

The Real ◽

Detection Model ◽

Bounding Boxes ◽

The Given ◽

Single Neural Network

Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These boxes are weighted by the predicted probabilities. After non max-suppression it gives the result of recognized objects together with bounding boxes. Yolo trains and directly executes object detection on full images.

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