Almal Shubh, Bagepalli Apoorva Reddy, Dutta Prajjwal, Chaki Jyotismita
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PeerJ Comput Sci. 2023 Feb 15;9:e1250. doi: 10.7717/peerj-cs.1250. eCollection 2023.
Animal illness is a disturbance in an animal's natural condition that disrupts or changes critical functions. Concern over animal illnesses stretches back to the earliest human interactions with animals and is mirrored in early religious and magical beliefs. Animals have long been recognized as disease carriers. Man has most likely been bitten, stung, kicked, and gored by animals for as long as he has been alive; also, early man fell ill or died after consuming the flesh of deceased animals. Man has recently learned that numerous invertebrates are capable of transferring disease-causing pathogens from man to man or from other vertebrates to man. These animals, which function as hosts, agents, and carriers of disease, play a significant role in the transmission and perpetuation of human sickness. Thus, there is a need to detect unhealthy animals from a whole group of animals.
In this study, a deep learning-based method is used to detect or separate out healthy-unhealthy animals. As the dataset contains a smaller number of images, an image augmentation-based method is used prior to feed the data in the deep learning network. Flipping, scale-up, sale-down and orientation is applied in the combination of one to four to increase the number of images as well as to make the system robust from these variations. One fuzzy-based brightness correction method is proposed to correct the brightness of the image. Lastly, semi-supervised generative adversarial network (SGAN) is used to detect the healthy-unhealthy animal images. As per our knowledge, this is the first article which is prepared to detect healthy-unhealthy animal images.
The outcome of the method is tested on augmented COCO dataset and achieved 91% accuracy which is showing the efficacy of the method.
A novel two-fold animal healthy-unhealthy detection system is proposed in this study. The result gives 91.4% accuracy of the model and detects the health of the animals in the pictures accurately. Thus, the system improved the literature on healthy-unhealthy animal detection techniques. The proposed approach may effortlessly be utilized in many computer vision systems that could be confused by the existence of a healthy-unhealthy animal.
动物疾病是动物自然状态的一种紊乱，会扰乱或改变其关键功能。对动物疾病的关注可以追溯到人类与动物最早的互动时期，并反映在早期的宗教和魔法信仰中。长期以来，动物一直被视为疾病携带者。人类自诞生以来很可能就被动物咬伤、蜇伤、踢伤和顶伤；此外，早期人类在食用病死动物的肉后会生病或死亡。最近，人类了解到许多无脊椎动物能够将致病病原体在人与人之间或从其他脊椎动物传播给人类。这些作为疾病宿主、媒介和携带者的动物，在人类疾病的传播和延续中起着重要作用。因此，有必要从整群动物中检测出不健康的动物。
在本研究中，使用一种基于深度学习的方法来检测或区分健康与不健康的动物。由于数据集包含的图像数量较少，在将数据输入深度学习网络之前，使用了一种基于图像增强的方法。以一到四种组合的方式应用翻转、放大、缩小和旋转，以增加图像数量，并使系统对这些变化具有鲁棒性。提出了一种基于模糊的亮度校正方法来校正图像的亮度。最后，使用半监督生成对抗网络（SGAN）来检测健康与不健康的动物图像。据我们所知，这是第一篇准备用于检测健康与不健康动物图像的文章。
该方法的结果在增强的COCO数据集上进行了测试，准确率达到91%，表明了该方法的有效性。
本研究提出了一种新颖的双重动物健康与不健康检测系统。结果显示模型的准确率为91.4%，能够准确检测图片中动物的健康状况。因此，该系统改进了关于健康与不健康动物检测技术的文献。所提出的方法可以轻松应用于许多可能因健康与不健康动物的存在而产生混淆的计算机视觉系统中。
