EnsemblePigDet: Ensemble Deep Learning for Accurate Pig Detection

Self Cite

Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this “light-weight” method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board

Kim¹,

Suh²,

Lee³

et al. 2022

Self Cite

“…Recently, many studies have been conducted on automatic livestock monitoring using various methods. Some of these include wearable sensor-based [ 6 , 7 , 8 ], audio-based [ 9 , 10 , 11 , 12 , 13 ], or video-based methods [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Compared with sensor-based methods, video-based methods can provide a non-invasive, non-stressful, and intuitive way to monitor the behavior of an individual or a group of pigs where the sensor-based method fail to do.…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System

Zhao

Atif

Lee

et al. 2022

Self Cite

Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.

“…With the recent advances in the area of deep learning, convolutional neural network (CNN)-based applications achieved state-of-the-art results in various image and video object detection scenarios [18]. Here, the most frequently used detection approaches aim to localize an object of interest by computing a bounding box around the object [19][20][21]. Although these approaches work successfully for various problem settings, due to the overlapping of the predicted bounding boxes, their applicability is limited for the analysis of videos with high utilization rates and several pigs in a close environment [22,23].…”

Section: Introductionmentioning

confidence: 99%

Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts

Wutke

Heinrich

Das

et al. 2021

The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head–head and head–tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems.