IoT-based nano wireless sensor approach for detection of ships using mixed convolutional neural network approach

Ships and other maritime objects are often unable to endure the harsh and dynamic sea environment. Collecting real-time data and detecting these objects using various sensors such as RADARs, Synthetic Aperture RADARs, and mounted RADARs present significant challenges due to numerous influencing factors. To address this issue, our research aims to develop an Internet of Things (IoT)-based multi-scale and multi-scene ship identification system. This system leverages a multi-scale neural network integrated with a high-response convolutional neural network (CNN)-based Kalman filter architecture. To construct this model, we selected various ship categories and initially employed a base CNN model to develop a new model with different convolutional layers. Our approach utilizes mixed methods for tracking and detecting objects, with a focus on small ships. The dataset is processed through multiple neural network layers, and we implemented the Kalman filter to estimate and predict the ships’ positions. Additionally, using the YOLOv3 model, we achieved improved accuracy and reduced error rates through mathematical optimization. Our method utilizes a dataset of 5,604 samples and incorporates a hybrid approach with YOLOv3. Our model demonstrates significant improvements for both medium-sized and small ships. The proposed work provides both qualitative and quantitative advancements. Our model exceeded the best results from parallel experiments by 3.9% and 1.2% in terms of Average Precision (AP). Furthermore, YOLOv3 achieved a performance score of 97.34% across various metric parameters, while our proposed approach attained the highest scores of 97.8% and 94.87%, respectively.