Natural disasters devastate local communities and make search and rescue (SAR) slow for those directly affected. Especially in harsh conditions, the search and rescue of people can take several days, which may be life-threatening in some cases. Technology that allows faster analysis will improve search and rescue efforts both in search time and resource management. We developed a deep neural machine (DNM), a novel artificial intelligence technology that processes UAV imagery for detection of people and other objects of interest in harsh environments, specifically for aquatic and nautical search and rescue. The DMN architecture utilizes convolution, batch normalization, and MISH activation (CBM) blocks, cross-stage partial (CSP) network blocks for residual features, and spatial pyramid pooling (SPP) blocks for merging different levels of features. Augmented versions of various datasets with synthetic weather scenarios are used for effective training of the object detection system for enhanced performance in changing environments. The DNM based autonomous vision system provides robust detection of humans and other objects of interest in aquatic and nautical environments.