Bird detection system in field scene

Abstract

The invention discloses a detection method of a bird detection system in a field scene. The detection method comprises the following steps: S1, collecting video data in an actual scene and existing bird pictures and video data on a network; s2, performing data preprocessing on the collected bird data by using data preprocessing software; s3, performing data enhancement on the processed bird picture data set; s4, training a deep learning target detection model by using the bird data set after data enhancement and the background data set without birds; s5, deploying the trained target detection model on edge equipment in an actual field environment; s6, using a camera to collect video data of the bird protection area in the field scene; and S7, using a deep learning target detection model to detect a target according to an image, and returning a detection result to the central server. According to the invention, the cost of manual detection is reduced, the detection effect of the model is improved, a result in accordance with an actual scene is obtained, and the processing pressure of a central server is reduced.

Description

technical field [0001] The invention belongs to the technical field of bird detection methods, and in particular relates to a bird detection system in a field scene. Background technique [0002] The discovery and statistics of birds in protected areas has always been an important part of environmental protection. By recording the number of birds, the time of appearance and residence time of birds, it can effectively reflect the current health status of birds, the quality of the ecological environment of the protected area and other information, which is convenient for professionals to carry out environmental protection work. However, the traditional method is to manually classify and count the video data captured by the cameras in the environmental protection zone. This method requires experts to spend a lot of time processing the video data, which is time-consuming and labor-intensive, and the workload is heavy. Therefore, we can use the deep learning-based target detecti...

AI Technical Summary

Problems solved by technology

However, due to its own limitations, the calculation cost of the two-stage algorithm is too high, and the detection time of an image is too long, which is not suitable for practical applications. At the same time, the relative position information of the local target in the whole cannot be used.

Images