Substation Danger Sign Detection and Recognition using Convolutional Neural Networks

This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.

Section: Ref Models and Methodsmentioning

confidence: 99%

A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

Sajini,

Pushpa

2024

“…Rather than complicating the design of Deep Neural Networks (DNN) [9][10][11][12][18][19], a separate filter was developed to be placed between the camera and the CNN module. This filter mechanism enriches the image captured by the camera and feeds it to CNN models, as shown in Figure 2.…”

Section: B Riod Architecturementioning

confidence: 99%

RIOD:Reinforced Image-based Object Detection for Unruly Weather Conditions

Pavitha,

Rekha,

Safinaz

2024

Deep Neural Network (DNN) object detectors have proved their efficiency in the detection and classification of objects in normal weather. However, these models suffer a lot during bad weather conditions (foggy, rain, haze, night, etc.). This study presents a new scheme to reduce the aforementioned issue by attenuating the noise in the input image before feeding it to any kind of neural network-based object detector. In this study, the image optimization function transforms subpar-quality images due to bad weather into pictures with the optimal possible quality by estimating the proper illumination and transmission function. These optimized images showed improved object detection rates in the YOLOv4 and YOLOv5 models. This improvement in object detection was also noticed in the case of video input. This scheme was tested with images/videos from various weather conditions, and the results showed an encouraging improvement in detection rates.

“…These gates alleviate the problem of disappearing gradients that can occur in LSTMs [26]. This is a similar approach to [27] on the detection and recognition of danger signs, to [28] on prediction, and to [29] on the planning of routes using RNNs. The LSTM is guided and uses the historical context maintained by the forget gate.…”

Section: A the Bidirectional Long-short-term Memory (Bltsm) Model Of ...mentioning

confidence: 99%

The Use of Recurrent Nets for the Prediction of e-Commerce Sales

Aldhahri

2023

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance.