Cow behavior recognition based on image analysis and activities

For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.

Vibration-based hypervelocity impact identification and localization

Hypervelocity impact(HVI)vibration source identification and localization have found wide applications in many fields,such as manned spacecraft protection and machine tool collision damage detection and localization.In this paper,we study the synchrosqueezed transform(SST)algorithm and the texture color distribution(TCD)based HVI source identification and localization using impact images.The extracted SST and TCD image features are fused for HVI image representation.To achieve more accurate detection and localization,the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features.Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization.To demonstrate the effectiveness of the proposed algorithm,the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation.The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms.Finally,based on sensor distribution,an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.