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Our platform can identify every single object based on the input data. This work aims to improve the present model's detection accuracy rate by analyzing films for items. To "see" the bigger picture, we implement a specialized dark web CNN algorithm. The YOLO technique can also be used to anticipate the likelihood of a full image, making it ideal for speedy real-time object recognition. The suggested approach can estimate the object's size with much greater precision. Merging a tailored dark net convolutional neural network with the YOLO algorithm provides an efficient approach for estimating object scale. As mentioned in the section on object localization, the method first grids the image and then applies the image classification and localization technique to each cell. The entire image can be processed by this method. The detection accuracy is also improved by combining the coco model with the tensor flow.

Automatic ship detection in SAR imagery has been playing a significant role in the field of marine monitoring but great challenges still exist in real-time application. Despite the exciting progresses made by deep-learning techniques, most detectors failed to yield locations of fairly high quality. Moreover, the ships with variant sizes and aspects are easily omitted especially for small objects under complicated background. To alleviate the above problem, the authors propose an elaborately designed single shot detection framework combined with attention mechanism, which roughly locates the regions of interest via an automatically learned attentional map. This lay the foundation of accurate positioning of extremely small objects since the background interference can be effectively suppressed. Furthermore, a multi-level feature fusion module integrated in top-down and bottom-up manner is adopted to adequately aggregate features from not only adjacent but also distant layers. This strengthens local details and merge strong semantic information, enabling the generation of higher qualified anchors for the efficient detection of multi-scale and multi-orientated objects. Experiments on SAR ship dataset have achieved a promising result, surpassing current state-of-the-art methods.

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.

This research demonstrates a machine learning (ML) approach to array-based free-space optical (FSO) communication using mobile devices. Modern warfighters need non'radio frequency (RF) communication methods to eliminate the risks associated with RF communication, such as detection, eavesdropping, and jamming. FSO communications promises tremendous throughput among other advantages, such as low-probability of intercept/detect and resistance to jamming. However, atmospheric conditions significantly reduce achieved performance by introducing fading and noise on the channel. To increase channel resilience and throughput, we employ spatial codes using an array of lasers at the transmitter and train several ML models on the channel alphabet to provide efficient decoding at the receiver. We compare the performance of a Single Shot Detection (SSD) MobileNet model with a You-Only-Look-Once model during the training process, and we demonstrate data transfer over a proof-of-concept system using the trained SSD MobileNet model. We detail the hardware and software implementation for the proof-of-concept, which uses handheld mobile devices and an array of low-cost, low-power lasers. Future experimentation is planned to incorporate forward-error correction and testing over greater distances under realistic conditions. Lieutenant, United States Navy Approved for public release. distribution is unlimited

Background Visual impairments have become one of the most predominant problems for the last few decades. To keep doing their daily tasks, vision-impaired people usually seek help from others. An automated common object and currency recognition system can improve the safe movement and transaction activity of visually impaired people. Objective To develop a system that can identify indoor and outdoor objects, notify the users, and send all information to a remote server repeatedly at a fixed time interval. Methods The proposed system assists the visually impaired to recognize several objects and provides an audio message to aware the user. Four laser sensors are used in the system to detect the objects in the direction of the front, left, right and ground. The proposed system uses Single Shot Detector (SSD) model with MobileNet and Tensorflow-lite to recognize objects along with the currency note in the real-time scenario in both indoor and outdoor environments. Results Among 375 participants, 82% reacted that the price of the proposed system is reasonable, 13% treated as the cost is moderate and the rest 5% people responded that the cost is relatively high for them. In terms of size and weight, 73% reacted that the size and weight are considerable, 20% treated that the size is not suitable, and weight needs to lessen, and the rest 7% people responded that the system is bulky. Regarding input signal observation, 98% responded that they have heard the sound appropriately and the remaining 2% of individuals missed hearing the signal. Conclusions This paper represents an IoT-enabled automated object recognition system that simplifies the mobility problems of the visually impaired in indoor and outdoor environments. The overall accuracy of the proposed system in object detection and recognition is 99.31% and 98.43% respectively. In addition, the proposed system sends all processed data to a remote server through IoT.