Machine learning method and device based on intelligent fishing rod

Abstract

The invention provides a machine learning method based on an intelligent fishing rod, a terminal and a computer storage medium, and the method comprises the steps of building fish databases corresponding to different fishing scenes, increasing the robustness of the fish databases, building a machine learning fish image data classification model based on an expanded training data set, selecting the fishing scene in a fishing process, acquiring water area scene information, acquiring fish target information, and identifying a fish target. According to the invention, the fish condition images in the water are stably and accurately obtained, and the fish condition images in the water are classified and identified; the function that the intelligent fishing rod can obtain the fish condition in water and can also judge the type of the fish in water is realized.

Description

technical field [0001] The invention belongs to the field of outdoor sports, and in particular relates to an underwater fish image collection device based on a fishing rod and an automatic image classification method. Background technique [0002] Currently. The smart fishing rods on the market can use the acceleration sensor on the fishing line or the float to obtain the instantaneous downward pulling force generated when the fish bites the hook, so as to judge whether there is a fish biting the hook. Since its working principle is often based on whether the acceleration value collected by the acceleration sensor is greater than the preset threshold range to determine whether there is a fish biting the hook, the sensitivity of this type of device depends heavily on the preset threshold and the accuracy of the acceleration sensor. There is a large misjudgment rate in actual use. In addition, this type of device cannot judge whether it is fish or other sundries on the hook,...

AI Technical Summary

Problems solved by technology

Since its working principle is often based on whether the acceleration value collected by the acceleration sensor is greater than the preset threshold range to determine whether there is a fish biting the hook, the sensitivity of this type of device depends heavily on the preset threshold and the accuracy of the acceleration sensor. In actual use, there is a large misjudgment rate

In addition, this type of device cannot judge whether it is fish or other debris on the hook, let alone the type of fish in the water, and the user experience is poor

Images