<p>Object detection in Unmanned Aerial Vehicles (UAVs) is inherently challenging due to the wide variation in altitudes and viewpoints, coupled with the limited computational resources of onboard embedded systems. Traditional Convolutional Neural Networks (CNNs), while accurate, are often over-parameterized and inefficient for real-time UAV deployment due to their high computational and memory demands. Designed for all types of inputs, they perform redundant operations that strain the limited processing power, memory, and energy resources available on UAV platforms. This work addresses these limitations by modeling UAV object detection as a context-aware task, using altitude as a primary adaptive parameter. We train specialized CNN configurations for discrete altitude ranges, exploring the impact of key parameters—input image resolution, network width, and kernel size—on detection performance and efficiency. Dynamic parameter switching based on altitude enables resource-efficient deployment without compromising accuracy. Using two altitude-diverse datasets, we demonstrate that optimal CNN settings vary significantly across altitudes, underscoring the need for altitude-specific adaptation. We introduce a threshold-based switching mechanism and perform a detailed analysis of how varying these altitude thresholds affects both detection accuracy and system efficiency. Our results show that well-chosen thresholds result in minimal accuracy loss and maximal resource savings compared to an equivalent non-dynamic CNN. The proposed dynamic CNN framework offers a scalable, energy-efficient solution for UAV-based applications such as surveillance, search and rescue, and environmental monitoring, where adaptability to context is essential.</p>