Breakthroughs, discoveries, and DIY tips sent every weekday. Drones can be a divisive subject, but they do have their uses (beyond causing mass panic). Professional unpiloted aerial vehicles (UAVs) are already instrumental in conservation efforts and engineering projects, but even personal use drones do more than recording cool aerial shots of your vacation. In the case of a recent emergency in Florida, one man's drone helped save a teenager's life. Earlier this month, amateur shark fisherman Andrew Smith was convinced by a friend to come with them to Pensacola Beach after getting off from work.

Rip currents are strong, localized and narrow currents of water that flow outwards into the sea, causing numerous beach-related injuries and fatalities worldwide. Accurate identification of rip currents remains challenging due to their amorphous nature and the lack of annotated data, which often requires expert knowledge. To address these issues, we present RipVIS, a large-scale video instance segmentation benchmark explicitly designed for rip current segmentation. RipVIS is an order of magnitude larger than previous datasets, featuring $184$ videos ($212,328$ frames), of which $150$ videos ($163,528$ frames) are with rip currents, collected from various sources, including drones, mobile phones, and fixed beach cameras. Our dataset encompasses diverse visual contexts, such as wave-breaking patterns, sediment flows, and water color variations, across multiple global locations, including USA, Mexico, Costa Rica, Portugal, Italy, Greece, Romania, Sri Lanka, Australia and New Zealand. Most videos are annotated at $5$ FPS to ensure accuracy in dynamic scenarios, supplemented by an additional $34$ videos ($48,800$ frames) without rip currents. We conduct comprehensive experiments with Mask R-CNN, Cascade Mask R-CNN, SparseInst and YOLO11, fine-tuning these models for the task of rip current segmentation. Results are reported in terms of multiple metrics, with a particular focus on the $F_2$ score to prioritize recall and reduce false negatives. To enhance segmentation performance, we introduce a novel post-processing step based on Temporal Confidence Aggregation (TCA). RipVIS aims to set a new standard for rip current segmentation, contributing towards safer beach environments. We offer a benchmark website to share data, models, and results with the research community, encouraging ongoing collaboration and future contributions, at https://ripvis.ai.

Your trip to the beach could someday be a lot safer, thanks to artificial intelligence. Researchers at the University of California Santa Cruz, led by Professor Alex Pang, are developing potentially life-saving A.I. algorithms geared toward detecting and monitoring potential dangers along the shoreline, according to the Santa Cruz Sentinel. The life-saving technology could also alert lifeguards of potential hazards and detect rip currents or riptides, which, according to water rescues and safety expert Gerry Dworkin, account for 80% of ocean lifeguard interventions. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? Flags warn that the beach is closed to swimmers at Rockaway Beach in New York as high surf from Hurricane Franklin delivers strong rip tides and large waves to most of the eastern seaboard on August 31, 2023 in New York City.

Lifeguards in Flagler Beach, Florida, formed a human chain to rescue a boogie boarder who appeared to have drifted far from shore. A team of lifeguards in Florida were captured on drone video over the weekend forming a human chain to rescue a boogie boarder from a rip current that was pulling him out into the ocean. Joe Osborne was on break from his job at a tattoo parlor in Flagler Beach when he decided to fly his drone over the ocean and captured the lifeguards in action. "I was actually kind of impressed," Osborne told FOX35 Orlando. "It was definitely a rehearsed thing ... with their buoys and their lines, and they use them in unison. I thought it was very neat."

We present a hybrid machine learning and flow analysis feature detection method, RipViz, to extract rip currents from stationary videos. Rip currents are dangerous strong currents that can drag beachgoers out to sea. Most people are either unaware of them or do not know what they look like. In some instances, even trained personnel such as lifeguards have difficulty identifying them. RipViz produces a simple, easy to understand visualization of rip location overlaid on the source video. With RipViz, we first obtain an unsteady 2D vector field from the stationary video using optical flow. Movement at each pixel is analyzed over time. At each seed point, sequences of short pathlines, rather a single long pathline, are traced across the frames of the video to better capture the quasi-periodic flow behavior of wave activity. Because of the motion on the beach, the surf zone, and the surrounding areas, these pathlines may still appear very cluttered and incomprehensible. Furthermore, lay audiences are not familiar with pathlines and may not know how to interpret them. To address this, we treat rip currents as a flow anomaly in an otherwise normal flow. To learn about the normal flow behavior, we train an LSTM autoencoder with pathline sequences from normal ocean, foreground, and background movements. During test time, we use the trained LSTM autoencoder to detect anomalous pathlines (i.e., those in the rip zone). The origination points of such anomalous pathlines, over the course of the video, are then presented as points within the rip zone. RipViz is fully automated and does not require user input. Feedback from domain expert suggests that RipViz has the potential for wider use.

Beachgoers could be safer thanks to a new technology with the potential to give real-time updates of rip currents. Rip currents are narrow, fast-moving segments of water that travel away from the shore. They can reach speeds of 2.5 meters per second, which is quicker than the fastest Olympic swimmer. NIWA and Surf Life Saving New Zealand (SLSNZ) have developed a state-of-the-art rip current identification tool using artificial intelligence (AI) and deep learning. The tool showed around 90% accuracy detecting rip currents in videos and images in trials.

The system uses cameras that help lifeguards keep swimmers away from a hazardous situation near the shoreline. Every year, rip currents, undertows, and rip tides kill around 100 beachgoers in the United States. These bodies of water seep away from the shore through deep channels and are very common on nearly any worldwide beach. They are often indistinguishable in the eyes of a swimmer and even an inexperienced water sports enthusiast. And most people don't know what to do to avoid and survive a rip current.

Drowning is the third leading cause of accidental death, according to World Health Organization (WHO) data, with an estimated 320,000 fatalities each year globally. While lifeguards play a crucial role in helping safeguard beaches and pools, the human eye struggles to spot swimmers in distress in large crowds or at a distance -- with or without the help of binoculars. Sightbit is harnessing AI to alert lifeguards to potential drowning incidents, as well as flagging other hazardous situations, such as unattended children and rip currents. Founded in 2019, Israel-based Sightbit is a spinout from Ben-Gurion University of the Negev (BGU). The public research university invests in alumni via its Cactus Capital VC fund and has provided pre-seed funding to Sightbit, which is currently raising additional funds as part of a seed round.