The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced.

Operational forest monitoring often requires fine-detail information in the form of an orthomosaic, created by stitching overlapping nadir images captured by aerial platforms such as drones. RGB drone sensors are commonly used for low-cost, high-resolution imaging that is conducive to effective orthomosaicking, but only capture visible light. Thermal sensors, on the other hand, capture long-wave infrared radiation, which is useful for early pest detection among other applications. However, these lower-resolution images suffer from reduced contrast and lack of descriptive features for successful orthomosaicking, leading to gaps or swirling artifacts in the orthomosaic. To tackle this, we propose a thermal orthomosaicking workflow that leverages simultaneously acquired RGB images. The latter are used for producing a surface mesh via structure from motion, while thermal images are only used to texture this mesh and yield a thermal orthomosaic. Prior to texturing, RGB-thermal image pairs are co-registered using an affine transformation derived from a machine learning technique. On average, the individual RGB and thermal images achieve a mutual information of 0.2787 after co-registration using our technique, compared to 0.0591 before co-registration, and 0.1934 using manual co-registration. We show that the thermal orthomosaic generated from our workflow (1) is of better quality than other existing methods, (2) is geometrically aligned with the RGB orthomosaic, (3) preserves radiometric information (i.e., surface temperatures) from the original thermal imagery, and (4) enables easy transfer of downstream tasks—such as tree crown detection from the RGB to the thermal orthomosaic. We also provide an open-source tool that implements our workflow to facilitate usage and further development.