Abstract
Faster RCNN is a classic algorithm with high accuracy and a wide range of applications in the field of target detection, and Cascade RCNN is improved based on Faster RCNN. The article applies the Cascade RCNN method to the detection of marine ship targets. It improves the traditional Faster RCNN algorithm and extracts areas that may contain pedestrians through RPN. In this paper, a multi-layer cascade detector is used to distinguish and classify the target area, and an algorithm is designed to detect and verify the data set. In the end, it is concluded that the Cascade RCNN algorithm performs better than the traditional algorithm.
Highlights
The development and competition of the ocean by mankind runs through the history of the development of human civilization [1]
Recognition based on target image information has gradually become the focus of research in the field of ship target recognition [5]
This paper is based on the Cascade RCNN algorithm, aiming at the problems that ships usually encounter at sea in harsh environments such as rain, snow, fog, and other interference factors such as reefs
Summary
The development and competition of the ocean by mankind runs through the history of the development of human civilization [1]. When the existing intelligent recognition algorithm performs image recognition, the recognition accuracy is often low due to the poor weather conditions of the image to be recognized, the complex shore-based background [3], and the small ship target to be recognized. This paper is based on the Cascade RCNN algorithm, aiming at the problems that ships usually encounter at sea in harsh environments such as rain, snow, fog, and other interference factors such as reefs. Ship targets are blocked, and the targets are small, and it is difficult to identify and locate with high accuracy. This paper proposes that the algorithm model combined with ships has significant advantages over the traditional Faster RCNN, which can detect and identify ships and ship categories in complex environments with high precision and accuracy
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