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

Abstract

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.

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

“…The image data used by the system were transmitted at a frame rate of 21 fps from a top-down angle RGB camera installed in the ceiling to minimize occlusions and overlapping with people and other objects. This angle also provides an intuitive way of localizing the objects of interest inside a room and studying their movements, making it commonly used in monitoring systems [ 49 , 50 , 51 , 52 , 56 ]. In this module, the collected RGB images are resized to .…”

“…Owing to recent advances in deep learning, the field of object detection has shown continuous improvements in speed and performance and led to the development of methods and models targeting several areas of research and covering different scenarios [ 57 , 58 ]. Among the existing object detection models, You Only Look Once (YOLO) [ 59 ] and its variants have attracted considerable attention because of their performance and real-time inference speed, making them a popular choice for monitoring systems [ 37 , 56 , 60 , 61 ].…”

StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information

“…Testing the pig object detection under lighter hardware is also considered for overall process speed [ 34 ]. Reducing image noise can increase overall image quality, thus improving the detection accuracy for pig monitoring [ 35 , 36 ]. There are more pig farm images on different pig pen environments like a camera is installed with top-view [ 19 ] or tilted-view [ 21 , 30 ], but facility has not presented.…”

“…Thus, managing each pig individually to their health and welfare needs is not an easy task. To reduce management workload, many studies have reported the use of surveillance techniques to address health and welfare problems [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Therefore, the use of object detection [ 37 ] to detect pigs by means of a surveillance camera can reduce the management workload within a pig pen.…”

“…However, the object detection technology locates the appearance of each object within an image [ 38 ]. Consequently, whenever a pig object is occluded by a complex facility (e.g., a feeder), it cannot be sufficiently identified by existing object detectors, thus reducing the overall detection accuracy [ 27 , 34 , 35 , 36 ]. As pigs are regularly within the proximity of feeder facilities for nourishment, they are frequently occluded in pig pen images [ 11 , 28 ].…”