Smart Animal Detection and Counting Framework for Monitoring Livestock in an Autonomous Unmanned Ground Vehicle Using Restricted Supervised Learning and Image Fusion

Automated livestock monitoring is a promising solution for vast and isolated farmlands or cattle stations. The advancement in sensor technology and the rise of unmanned systems have paved the way for the automated systems. In this work, we propose an Unmanned Ground Vehicle (UGV) based livestock detection-counting system for fusion images using restricted supervised learning technique. For image fusion, we propose Dual-scale image Decomposition based Fusion technique (DDF) that fuses visible and thermal images. To reduce the difficulty of ground truth annotation, we introduce Seed Labels focused Object Detector (SLOD) that carefully propagates the annotation to all the object instances in the training images. Further, we propose a novel Restricted Supervised Learning (RSL) technique that produces competitive results with minimal training data. Experimental results show that the proposed RSL is more efficient and accurate when compared to other learning techniques (fully and weakly supervised). On the test data, with only five training images and five seed labels, the restricted supervised learning has improved the average precision from 4.05% (using fully supervised learning) to 80.58% (using restricted supervised learning). With 50 seed labels, the average precision is further boosted to 91.56%. The proposed model is extensively tested on benchmark animal datasets and has achieved an average accuracy of 98.3%.