This paper aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans' indoor detection, most of the work has been focused on counting people and crowd measurement for consumer business applications. Our objective is to develop a reliable approach for humans' indoor detection and localization aiming at avoiding collisions inside a mixed Industry 4.0 manned and unmanned environment, so that to enhance the personal and equipment safety and to prevent unwanted intrusions. An original aspect of our research is that we have worked on the real time estimation of humans' and moving machines' positions, while addressing the problems of multipath components and noise clutter detection. A multi-pulse constant false alarm rate detection algorithm is also proposed for removing the misdetections due to heavy clutter components in the indoor environment. Four impulse radio ultrawideband transceivers are placed in a specific geometry and data fusion is performed to reduce the influence of multipath and noise on the detection process. A convolutional neural network (CNN) is then used to extract the patterns corresponding to a moving machine and humans and classify them accordingly. Experiments have been carried out in two different indoor environments to demonstrate the performance of the proposed algorithms.