Abstract
Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.
Highlights
In contrary to machines, it is easy for humans to detect, classify, and identify objects that are present in their surroundings irrespective of how they are positioned, aligned, etc.These objects can be identified even if they are misplaced, or they have different visuals
The dataset consists of only two attributes, namely image ID and encoded pixels, which are used for the ship detection and visualization purpose
The developed model is accurate with real-time data and performs well under adverse climatic conditions due to the use of You Only Look Once version 3 (YOLOv3), which performs detection at three different layers
Summary
It is easy for humans to detect, classify, and identify objects that are present in their surroundings irrespective of how they are positioned, aligned, etc. These objects can be identified even if they are misplaced, or they have different visuals. If the same process is to be carried out through a machine, a great deal of computational work and energy is required to process the information related to the object and to identify to which category the object belongs to Machines can handle this sort of object detection by using either an image input or a video input. Each of the stages involved in object detection consists of many strategies that provide different performances in different circumstances
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