Abstract
In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.
Highlights
The information age has witnessed the rapid development of wireless network technology, which has attracted the attention of researchers and practitioners due to its unique characteristics such as flexible structure and efficiency
This paper proposes object detection technique to detect objects in real time with a model that can be executed on any device in any environment
We summarize the details of faster RCNN and You Only Look Once (YOLO) v3 architecture as they are directly relevant to our proposed method
Summary
The information age has witnessed the rapid development of wireless network technology, which has attracted the attention of researchers and practitioners due to its unique characteristics such as flexible structure and efficiency. The advent of 5G network will further enable the greater development and more advanced applications of wireless network technology. The future generations of wireless networks will provide strong support for related applications such as Internet of Things (IoT) and virtual reality (VR). Many of these applications connect to each other and transmit information within networks based on the detection of specific target objects. In order to achieve a comprehensive network connection between people and people, things and people, and things and things, one of the key tasks of future applications is to identify the target in a real-time manner in the wireless networks [1]
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