CN-121527814-B - Intelligent bird recognition and observation system based on edge calculation

CN121527814BCN 121527814 BCN121527814 BCN 121527814BCN-121527814-B

Abstract

The invention relates to the technical field of artificial intelligence and discloses an intelligent bird recognition and observation system based on edge calculation. The processor operates the bird detection module to locate a bird target, the bird micro-feature extraction module extracts micro-feature vectors, the fine-granularity bird recognition module recognizes species and evaluates uncertainty scores, the active perception decision unit generates acquisition instructions according to the uncertainty scores, the intelligent camera control module controls the sensor to acquire high-value images according to the instructions, the bird behavior analysis module recognizes dynamic behaviors, and the lightweight visual language interaction module answers natural language questions of users. The invention builds an active perception closed loop, combines dynamic behavior analysis, solves the problems of insufficient intelligence and strong network dependence, and improves the observation accuracy, depth and automation level.

Inventors

TANG JIEHAO
LIU YINGLONG
CHEN RUITIAN
ZHANG FENG

Assignees

深圳市联合光学技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260119

Claims (9)

1. Intelligent bird recognition and observation system based on edge calculation, characterized by comprising: the bird detection module is used for preprocessing the image data acquired by the image sensor and positioning a bird target based on the preprocessed image data; The bird micro-feature extraction module is used for extracting micro-feature vectors representing individual features of the bird target aiming at the bird target; the fine-grain bird recognition module is used for fusing the image features of the bird targets and the micro feature vectors to recognize species recognition results of birds and evaluating uncertainty scores based on the species recognition results; The active perception decision unit is used for generating an active data acquisition instruction when the uncertainty score is higher than a preset threshold value; the intelligent camera control module is used for controlling the image sensor to execute preset data acquisition actions according to the active data acquisition instruction so as to acquire high-value image data; The uncertainty score The calculation formula of (2) is as follows: ; Wherein, the And Preset weight coefficients for balancing two uncertainty sources; For probability distribution of species Is an information entropy of (a); The degree of isolation of the micro-feature vector in the known feature space; the said The calculation formula of (2) is as follows: ; Wherein, the Is the total number of the preset species; Index for species category, ranging in value from 1 to ; For the probability distribution vector of species The first of (3) An element; the preset data acquisition action corresponding to the active data acquisition instruction is at least one of macro multi-angle shooting or preferential focusing of key biological characteristics.
2. The intelligent edge computing-based bird identification and viewing system of claim 1, wherein the step of the bird detection module preprocessing the image data collected by the image sensor comprises at least one of: frame downsampling, image normalization, or data enhancement to simulate different field environments.
3. The intelligent bird identification and observation system based on edge computing of claim 1, wherein the bird microfeature extraction module is specifically configured to: Based on a self-supervision contrast learning paradigm, learning and extracting the micro-feature vectors which have distinguishing force on individual differences and are robust to gesture changes and local occlusion by pulling the distances of feature vectors of the same bird target under different views in a feature space and pushing the distances of feature vectors of different bird targets.
4. The edge computing-based intelligent bird identification and observation system of claim 1, wherein the fine-grained bird identification module is specifically configured to: Generating fusion features for identifying the bird species by stitching or attentively fusing the micro-feature vectors with visual features extracted from the image of the bird target.
5. The intelligent bird identification and observation system based on edge computing of claim 1, further comprising a federal online fine tuning module running thereon, the federal online fine tuning module configured to: Utilizing the high value image data and at least one of self-labeling samples, or expert correction samples provided via a user interaction layer, for which identification results are determined by the fine grain bird identification module to have a low uncertainty score; And continuously optimizing the local model of the bird micro-feature extraction module and the fine-granularity bird identification module.
6. The edge computing-based intelligent bird recognition and observation system of claim 1, wherein the fine-grained bird recognition module is trained by a knowledge distillation framework that guides learning of student models with large teacher models trained in the cloud for inheriting generalization ability of the teacher models while maintaining lightweight.
7. The intelligent bird identification and observation system based on edge computing of claim 1, further comprising a bird behavior analysis module running on the system, the bird behavior analysis module configured to receive a sequence of successive images of the bird target and to identify dynamic behaviors of the bird target via a spatiotemporal analysis model.
8. The intelligent bird recognition and observation system based on edge computing of claim 7, wherein the intelligent camera control module is further configured to control the image sensor to perform high-speed continuous shooting or record video clips when the bird behavior analysis module recognizes a preset critical behavior.
9. The intelligent bird recognition and observation system based on edge computing of claim 1, wherein a lightweight visual language interaction module is further operated on the system, and the lightweight visual language interaction module is used for generating text answers to natural language questions input through a user interaction layer by combining image data and recognition results of the bird targets.

Description

Intelligent bird recognition and observation system based on edge calculation Technical Field The invention relates to the technical field of artificial intelligence, in particular to an intelligent bird recognition and observation system based on edge calculation. Background Birds are key indicator species of ecological environment, and research on diversity and behavior patterns of birds is of great significance for biodiversity protection. With the increasing interest of the public in natural science and the increasing demand of professional ecological monitoring, automated and intelligent bird observation and recognition technology is becoming a research hotspot in the field, and aims to improve the efficiency and breadth of data acquisition through technical means. Currently, some intelligent bird identification schemes rely mainly on cloud computing architecture. The typical working mode is that the front-end image acquisition equipment is responsible for capturing bird images, then original or compressed image data is transmitted to a remote server through a network, and species identification and other analysis tasks are completed by the high-performance deep learning model deployed at the cloud. The mode can utilize strong computing power of the cloud to execute a complex recognition algorithm, and provides possibility for large-scale data analysis. However, this highly network-dependent cloud solution exposes many drawbacks in practical applications. Firstly, the delay of network transmission not only affects the real-time performance of the identification result, but also makes the system difficult to quickly respond to the instantaneous behavior of birds, and makes it difficult to realize the real-time closed-loop control of the front-end camera. Second, in remote field areas where many birds inhabit, network coverage tends to be unstable, and continuous transmission of large amounts of video data also brings high bandwidth and cloud service costs. In addition, most of the existing schemes stay at the static species identification level, lack of deep analysis capability on bird dynamic behaviors, and difficulty in actively adjusting an observation strategy according to the confidence level of an identification result, so that the degree of intelligence is limited, and high-quality and deep ecological data are difficult to obtain. Therefore, the invention provides an intelligent bird recognition and observation system based on edge calculation, which solves the defects in the prior art. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an intelligent bird recognition and observation system based on edge calculation, solves the problems of high recognition delay and strong cost and network dependence caused by network transmission in the existing cloud scheme, further overcomes the defect of insufficient intelligent depth, and realizes active perception closed-loop control based on recognition uncertainty and deep understanding from static species recognition to dynamic behavior analysis. In order to achieve the purpose, the intelligent bird recognition and observation system based on edge calculation comprises the following technical scheme: The bird detection module is used for preprocessing the image data acquired by the image sensor and positioning a bird target based on the preprocessed image data; The bird micro-feature extraction module is used for extracting micro-feature vectors representing individual features of the bird target aiming at the bird target; the fine-grain bird recognition module is used for fusing the image features of the bird targets and the micro feature vectors to recognize species recognition results of birds and evaluating uncertainty scores based on the species recognition results; The active perception decision unit is used for generating an active data acquisition instruction when the uncertainty score is higher than a preset threshold value; and the intelligent camera control module is used for controlling the image sensor to execute a preset data acquisition action according to the active data acquisition instruction so as to acquire high-value image data. Preferably, the step of preprocessing the image data collected by the image sensor by the bird detection module includes at least one of: frame downsampling, image normalization, or data enhancement to simulate different field environments. Preferably, the bird micro-feature extraction module is specifically configured to: Based on a self-supervision contrast learning paradigm, learning and extracting the micro-feature vectors which have distinguishing force on individual differences and are robust to gesture changes and local occlusion by pulling the distances of feature vectors of the same bird target under different views in a feature space and pushing the distances of feature vectors of different bird targets. Preferably, the fine-grained bird recognition module is spec