Computer vision has several lesser-known applications that extend beyond typical uses like facial recognition or autonomous vehicles. One example is environmental monitoring, where it helps track ecosystems and detect changes that are difficult for humans to observe. For instance, researchers use drones equipped with cameras to capture images of forests, which are then analyzed to identify deforestation patterns or invasive species. Similarly, computer vision can monitor coral reefs by analyzing underwater footage to assess bleaching events or fish populations. These systems often combine object detection and segmentation to quantify environmental shifts, providing actionable data for conservation efforts. Another niche application is analyzing satellite imagery to detect illegal fishing activities by identifying ship movements in protected areas.
In industrial settings, computer vision is used for predictive maintenance of infrastructure. For example, pipelines in oil and gas facilities are inspected using cameras mounted on robots or drones. Algorithms detect corrosion, cracks, or leaks by comparing current images to baseline data. Thermal imaging combined with vision systems can identify overheating components in electrical grids, preventing failures before they occur. Another use case is monitoring manufacturing equipment for wear and tear, such as analyzing the shape of cutting tools to determine when replacements are needed. These applications reduce downtime by automating inspections that would otherwise require manual labor or specialized sensors.
A third underappreciated area is assistive technology for people with disabilities. Computer vision enables tools like real-time captioning for the deaf by interpreting sign language through cameras and translating gestures into text. It also aids the visually impaired by describing scenes or reading text aloud using smartphone cameras. For example, apps like Seeing AI use object detection and optical character recognition to identify products, currency, or people. Additionally, systems can analyze facial expressions to help individuals with autism interpret social cues, improving communication. These solutions often require low-latency processing and robust models to handle diverse real-world conditions, making them both technically challenging and impactful.