To further the application of artificial intelligence techniques in agriculture, this study proposes an approach based on deep neural network to estimate the live weights of pigs in saw stalls. We design a neural network that uses the back of pigs in top-view depth images as the input and outputs the pig weights estimates. The proposed network, which is based on a Faster-RCNN network with an added regressive branch, integrates the pig detection and live weights regressive network into an end-to-end network. It simultaneously performs pig recognition, location and pig weights estimate. Alternating the training method optimises the proposed network. Image simulation using circles with various overlapping areas and radii is used to prove the efficacy of the proposed network. When the overlap area is greater than 30% of the total area, the proposed network is invalid. Real farm experiments were conducted for three months to construct the back of pigs in top-view depth image data set to train the proposed network. The test results not only prove the relationship between size of back area and pig weights, but also verify that the proposed neural network can accurately estimate pig weights. The study will promote the application of intelligent technique in the livestock farming, and provides some references for intelligent weighing researchers. INDEX TERMS Digital image, deep learning, live weights estimation, object recognition. YAN CANG was born in Harbin, China, in 1978. She received the B.S. degree in material engineering from the University of Jiamusi, Heilongjiang, China, in 2000, and the M.S. degree in power engineering and the Ph.D. degree in information and communication engineering from Harbin Engineering University, Harbin, China, in 2003 and 2008, respectively. She was a Lecture with Harbin Engineering University. She holds one patent. Her research interests include artificial intelligent algorithm and image signal processing. HENGXIANG HE was born in Zhoushan, Zhejiang, China, in 1991. He received the B.S. and M.S. degrees in information and communication engineering from Harbin Engineering University, Heilongjiang, China, in 2015, where he is currently pursuing the Ph.D. degree in information and communication engineering. He did part-time job with Focused Loong Technology Company, Ltd., in 2017. His research interests include the distributed computation, artificial intelligent algorithm, and image signal processing.

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The wave is an important hydrological element in marine research. Accurately describing the characteristics of waves is therefore significant to the study of marine power. The contents of this article are as follows: (1) a wave height measurement system using binocular cameras is proposed, and the small tank experiments are conducted to prove the efficacy of the proposed system; (2) based on the scale invariant feature transition (SIFT) algorithm, sub-pixel Harris corners are calculated in the difference-of-Gaussian (DOG) space to locate key points more accurately; and (3) a bi-directional epipolar constraint is employed to decrease the mismatch rate and computation time.

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