Accurate and rapid image segmentation method for bayberry automatic picking via machine learning

Due to the short ripening period and complex picking environment,bayberry generally relies on mechanical equipment for picking,especially the automatic picking system guided by vision.Thus,it is crucial to locate the bayberry in the view accurately and rapidly.Although efforts have been made,the existing methods are difficult to implement due to the limited amount of data and the processing speed.In this study,an accurate and rapid segmentation method based on machine learning was proposed to address this problem.First,the images collected by the visual guidance system were pre-processed by contrast-limited adaptive histogram equalization(CLAHE)based on the Y component of the YUV color space.Taking advantage of the color difference map of RB and RG for the segmentation of different colors,an adaptive color difference map foreground segmentation method was then adopted for bayberry region foreground segmentation.Finally,distance transforms and marking control watershed methods were exploited to achieve single bayberry fruit segmentation.Furthermore,with the help of the convex hull theory and fruit shape characteristics,the irregular background interference areas were filtered out,which improved the accuracy of bayberry segmentation performance.The experimental results show that this method can achieve better segmentation of bayberry in complex orchard environment with an accuracy of 97.4%and only takes 0.136 s to calculate once.

Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.

Automatic onset phase picking for portable seismic array observation

Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike＇s information criterion （AIC） for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average （STA/LTA） technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections.