This paper presents a framework for autonomous UAV navigation and target detection in environments with Global Navigation Satellite System (GNSS) denial and low visibility. The framework is designed to enable UAVs to operate effectively in challenging conditions, such as those encountered in wildlife monitoring, disaster management, and emergency Search and Rescue (SAR) operations. The navigation and target detection problem is formulated as an Autonomous Sequential Decision Problem (SDP) using a Partially Observable Markov Decision Process (POMDP), and the Adaptive Belief Tree (ABT) algorithm is used to solve it. The framework is tested in simulations and real-life scenarios using a 5 kg UAV equipped with a thermal camera and an onboard computer. The results demonstrate the robustness of the framework in exploring and detecting targets under different visibility conditions, highlighting its potential for enhancing the capabilities of autonomous UAVs in critical applications.This paper presents a framework for autonomous UAV navigation and target detection in environments with Global Navigation Satellite System (GNSS) denial and low visibility. The framework is designed to enable UAVs to operate effectively in challenging conditions, such as those encountered in wildlife monitoring, disaster management, and emergency Search and Rescue (SAR) operations. The navigation and target detection problem is formulated as an Autonomous Sequential Decision Problem (SDP) using a Partially Observable Markov Decision Process (POMDP), and the Adaptive Belief Tree (ABT) algorithm is used to solve it. The framework is tested in simulations and real-life scenarios using a 5 kg UAV equipped with a thermal camera and an onboard computer. The results demonstrate the robustness of the framework in exploring and detecting targets under different visibility conditions, highlighting its potential for enhancing the capabilities of autonomous UAVs in critical applications.