DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

Complex automated monitoring of the hydrological and hydrobiological state of basinal freshwater lake Krasilovskoye (Altai Region)

The results of a comprehensive automated monitoring for the hydrological and hydrobiological state of a freshwater Lake Krasilovskoe, conducted since 2013 are given. The experimental dashboard is considered, including the atmospheric-soil measuring complex (ASMC), developed and created in the IMCES SB RAS, Tomsk. An analysis of the dynamics of the lake level during years with contrasting hydrometeorological conditions was performed, which made it possible to identify the main factors that determine the level mode in the spring. In the hydrobiological terms, the dynamics of phytoplankton concentration at different depths in different seasons of the year were investigated. The results of processing Sentinel-1, 2 satellite data and data of natural observations on the distribution of chlorophyll “A” in the surface layer of water sections of the lake water area are discussed.

Research on Highway Slope Disaster Automated Monitoring Method Based on Video Image Processing

With the rapid development of China highway transportation network, a lot of highways have risen in the mountainous areas. And the risk of geological disasters is also increasing. Due to the instability of the geological structure and geological changes, slope landslides and collapses frequently occur. However, there is a lack of effective means and tools in the field of highway slope disaster monitoring. For large and important slopes, various information-based monitoring methods are used, but the effects and practicability are not satisfactory. This paper proposes a highway slope disaster automated monitoring research method based on multi-camera video image processing to solve the previous engineering problems of slope video monitoring. It can quickly identify slope disaster events with centimeter-level monitoring accuracy, which can meet the application requirements of highway engineering. Correspondingly, the disaster emergency response capabilities of highway operators can also be supported and improved.