Research on CNN for Anti-missile Object Detection Algorithm Based on Improved Attention Mechanism

2021 ◽  
Author(s):  
Tainian Song ◽  
Weiwei Qin ◽  
Zhuo Liang ◽  
Qingqiang Qin ◽  
Gang Liu
Keyword(s):  
Object Detection ◽  
Detection Algorithm ◽  
Attention Mechanism

scholarly journals Object Detection Algorithm Based on Multiheaded Attention

2019 ◽  
Vol 9 (9) ◽  
pp. 1829 ◽  
Author(s):  
Jie Jiang ◽  
Hui Xu ◽  
Shichang Zhang ◽  
Yujie Fang
Keyword(s):  
Object Detection ◽  
Linear Interpolation ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Visual Object ◽  
Single Shot ◽  
Object Class ◽  
Feature Information ◽  
Base Network ◽  
Detector Model

This study proposes a multiheaded object detection algorithm referred to as MANet. The main purpose of the study is to integrate feature layers of different scales based on the attention mechanism and to enhance contextual connections. To achieve this, we first replaced the feed-forward base network of the single-shot detector with the ResNet–101 (inspired by the Deconvolutional Single-Shot Detector) and then applied linear interpolation and the attention mechanism. The information of the feature layers at different scales was fused to improve the accuracy of target detection. The primary contributions of this study are the propositions of (a) a fusion attention mechanism, and (b) a multiheaded attention fusion method. Our final MANet detector model effectively unifies the feature information among the feature layers at different scales, thus enabling it to detect objects with different sizes and with higher precision. We used the 512 × 512 input MANet (the backbone is ResNet–101) to obtain a mean accuracy of 82.7% based on the PASCAL visual object class 2007 test. These results demonstrated that our proposed method yielded better accuracy than those provided by the conventional Single-shot detector (SSD) and other advanced detectors.

scholarly journals Road Object Detection of YOLO Algorithm with Attention Mechanism

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jiacheng Li ◽  
◽  
Huazhang Wang ◽  
Yuan Xu ◽  
Fan Liu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Spatial Attention ◽  
High Precision ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Automatic Driving ◽  
Key Features ◽  
Detection Effect

In auto-driving cars, incorrect object detection can lead to serious accidents, so high-precision object detection is the key to automatic driving. This paper improves on the YOLOv3 object detection algorithm, and introduces the channel attention mechanism and spatial attention mechanism into the feature extraction network, which is used to autonomously learn the weight of each channel, enhance key features, and suppress redundant features. Experimental results show that the detection effect of the improved network algorithm is significantly higher than that of the YOLOv3 algorithm.

scholarly journals A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Zhang ◽  
Fei Xie ◽  
Lei Huang ◽  
Jianjun Shi ◽  
Jiale Yang ◽  
...  
Keyword(s):  
Object Detection ◽  
Defect Detection ◽  
Detection Algorithm ◽  
Industrial Applications ◽  
Attention Mechanism ◽  
Circuit Board ◽  
High Rate ◽  
Model Parameters ◽  
Small Object ◽  
One Stage

Normally functioning and complete printed circuit board (PCB) can ensure the safety and reliability of electronic equipment. PCB defect detection is extremely important in the field of industrial inspection. For traditional methods of PCB inspection, such as contact detection, are likely to damage the PCB surface and have high rate of erroneous detection. In recent years, methods of detection through image processing and machine learning have gradually been put into use. However, PCB inspection is still an extremely challenging task due to the small defects and the complex background. To solve this problem, a lightweight one-stage defect detection network based on dual attention mechanism and Path Aggregation Feature Pyramid Network (PAFPN) has been proposed. At present, some methods of defect detection in industrial applications are often based on object detection algorithms in the field of deep learning. Through comparative experiments, compared with the Faster R-CNN and YOLO v3 which are usually used in the current industrial detection, the inference time of our method are reduced by 17.46 milliseconds (ms) and 4.75 ms, and the amount of model parameters is greatly reduced. It is only 4.42 M, which is more suitable for industrial fields and embedded development systems. Compared with the common one-stage object detection algorithm Fully Convolutional One-Stage Object Detection (FCOS), mean Average Precision (mAP) is increased by 9.1%, and the amount of model parameters has been reduced by 86.12%.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Detection of sharp objects using deep neural network based object detection algorithm

2020 ◽  
Author(s):  
R Kayalvizhi ◽  
S Malarvizhi ◽  
Siddhartha Dhar Choudhury ◽  
Anita Topkar ◽  
P Vijayakumar
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Deep Neural Network ◽  
Detection Algorithm

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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