scholarly journals Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Novendra Setyawan ◽  
Nuralif Mardiyah ◽  
Khusnul Hidayat ◽  
Nurhadi Nurhadi ◽  
Zulfatman Has
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Omnidirectional Vision ◽  
Soccer Robot

scholarly journals Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot

2018 ◽  
Vol 5 (5) ◽  
Author(s):  
Novendra Setyawan ◽  
Nuralif Mardiyah ◽  
Khusnul Hidayat ◽  
Nurhadi Nurhadi ◽  
Zulfatman Has
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Omnidirectional Vision ◽  
Soccer Robot

PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)

2019 ◽  
Vol 17 (1) ◽  
pp. 69-76
Author(s):  
Mohammad Shiddiq Ghozali
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Object Detection ◽  
Open Source ◽  
Automated Teller Machine

Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat CCTV akan tetapi penjahat menggunakan alat bantu untuk menutupi wajahnya seperti helm, topi, masker dan kacamata hitam. Biasanya didepan pintu masuk ATM terpampang larangan untuk tidak menggunakan helm, topi, masker dan kacamata hitam serta tidak membawa rokok. Akan tetapi larangan itu masih tetap ada yang melanggar, dikarenakan tidak ada tindak lanjut ketika seseorang menggunakan benda-benda yang dilarang dibawa kedalam ATM. Oleh karena itu penulis membuat sistem pendeteksi obyek di bidang Kecerdasan Buatan untuk mendeteksi benda-benda yang dilarang digunakan ketika berada di ATM. Salah satu metode yang digunakan untuk menciptakan Object Detection yaitu You Only Look Once (YOLO). Implementasi ide ini tersedia pada DARKNET (open source neural network). Cara kerja YOLO yaitu dengan melihat seluruh gambar sekali, kemudian melewati jaringan saraf sekali langsung mendeteksi object yang ada. Oleh karena itu disebut You Only Look Once (YOLO). Pada penelitian ini, penulis membuat sistem yang masih dalam bentuk pengembangan, sehingga menjalankannya masih menggunakan command prompt. Keywords : Automated Teller Machine (ATM), Kecerdasan Buatan, Pendeteksi Obyek, You Only Look Once (YOLO)  

scholarly journals Geometric property-based convolutional neural network for indoor object detection

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199332
Author(s):  
Xintao Ding ◽  
Boquan Li ◽  
Jinbao Wang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Geometric Property ◽  
Ground Truth ◽  
Geometric Constraints ◽  
Depth Information ◽  
Training Set ◽  
Object Knowledge ◽  
The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

A dual neural network for object detection in UAV images

2021 ◽  
Vol 443 ◽  
pp. 292-301
Author(s):  
Gangyi Tian ◽  
Jianran Liu ◽  
Wenyuan Yang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Uav Images

scholarly journals The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1737
Author(s):  
Wooseop Lee ◽  
Min-Hee Kang ◽  
Jaein Song ◽  
Keeyeon Hwang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Processing Speed ◽  
Model Comparison ◽  
Distance Estimation ◽  
Visual Object ◽  
Suitable Model ◽  
Automated Vehicles ◽  
Automated Driving

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

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