Object detection, the task of identifying and localizing objects within images or video, is a rapidly growing field that has become fundamental to numerous applications, including autonomous vehicles, surveillance systems, agricultural monitoring, and infrastructure assessment. While traditional visible-light cameras operating in the Red-Green-Blue (RGB) wavelengths excel at providing detailed color and texture information under good lighting conditions, they become ineffective in challenging conditions, such as low light, nighttime, or adverse weather. Long-Wave Infrared (LWIR) sensors complement these capabilities by detecting thermal radiation naturally emitted by objects, enabling detection regardless of ambient lighting conditions. Because of their complementary strengths, the fusion of RGB and LWIR modalities creates detection systems that maintain robust performance across diverse operational scenarios. Modern object detection leverages deep learning approaches, such as Convolutional Neural Networks (CNNs) and emerging transformer architectures, that have revolutionized the extraction of features and automatic classification of objects. This entry provides an overview of RGB and LWIR sensor technologies, neural network-based object detection methods, and their combined applications across domains, including autonomous driving, precision agriculture, infrastructure monitoring, maritime surveillance, and defense applications.