WasteVision AI, a frontrunner in waste management technology, has announced a partnership with Keymakr, a leading data annotation company. By integrating Keymakr’s advanced video and image annotation expertise and tools, WasteVision AI aims to bolster its contamination detection capabilities – all while sidelining the driver from the process.
“Collecting waste is fraught with peril… Every week, we lose a worker in the US waste industry to accidents, and twice weekly, collection services are involved in fatal mishaps,” explained David Biderman, Executive Director and CEO of the Solid Waste Association of North America (SWANA). “From hazardous items like batteries and syringes in trash to external threats like weather and distractions, the challenges are plenty. The increased volume of waste only compounds these risks.”
Addressing these issues head-on, WasteVision AI’s innovative solution utilizes computer vision to detect contamination. Once identified, the system immediately notifies operators through all kinds of existing alerting systems, including SMS, direct mailers, and emails. Thus, the waste service companies eliminate the need for drivers to frequently check for contamination, and enhance overall safety.
But contamination isn’t the only challenge. A 2022 OECD report highlighted the escalating global plastic waste crisis. The world now produces twice the plastic waste compared to two decades ago, with a vast majority ending up in landfills, burned, or polluting the environment. Only a paltry 9% gets recycled. While macroplastics remain a primary concern, the leakage of microplastics, tiny synthetic fragments, is an emerging threat.
This collaboration between WasteVision AI and Keymakr isn’t just a merger of tech and data. It’s a strategic move towards a more sustainable and safe waste management future.
WasteVision AI’s Tech Turns Garbage Trucks into Smart Waste Detectors
Globally, while only a mere 9% of plastic waste is recycled, a concerning 22% is mismanaged. Addressing this concern, Tony Genovese, the CTO of WasteVision AI, dives into the specifics of their game-changing technology.
“Most might assume that integrating sorting technology directly into garbage trucks is a stretch, but that’s precisely where our innovation comes into play,” says Genovese. “While certain facilities might mix all types of waste, our tech, installed right on the garbage truck, evaluates every single can it collects. We’re then able to associate this evaluation with individual stops and the specific hauler.”
“Our platform isn’t limited to recycling trucks or general waste trucks; it’s all about intelligent waste categorization,” added Genovese. “With this data-driven approach, we can discern if waste has been disposed of correctly. This means identifying whether recyclables have ended up in general trash or vice versa. And yes, we have metrics to pinpoint just how much of each type of waste there is.”
From a business standpoint, the benefits for haulers are evident. “We’ve observed a staggering 15 to 20 times ROI for haulers right off the bat. This isn’t just a step forward for the environment; it’s a win for the bottom line too.”
WasteVision AI’s Tech: Beyond Profit, Shaping a Greener Tomorrow
While WasteVision AI’s innovations promise substantial financial returns for haulers, the implications for our planet are even more profound. “It’s not just about the money,” emphasized Genovese. “It’s about setting a new environmental precedent.”
“By providing haulers the tools to efficiently implement fines for improper waste disposal, we’re ensuring accountability. It’s been a challenge for haulers to hold waste generators responsible in a consistent and effective manner. But with our tech, we’re priming a behavioral change. When individuals start disposing of trash and recyclables correctly, this positive behavior will ripple upwards, leading to cleaner recycling not just locally but globally.”
Cleaner recycling isn’t just a lofty ideal; it’s underpinned by precision. Genovese underscores the gravity of accuracy, especially when it comes to annotation: “Recognition of different materials must be impeccable. While missing an opportunity to identify contaminants might be forgivable, if you’re fining a customer for contamination, accuracy is paramount. Issuing a fine demands 100% certainty, and that level of precision is what we’re committed to delivering.”
WasteVision AI’s Tech on the Move: How Many Trucks? It’s More About Impact than Numbers
When asked about how many trucks have adopted WasteVision AI’s transformative tech, Genovese chooses to focus on the broader picture. “Quantifying the exact number of trucks is challenging. Ideally, we’d be on all of them, but that might be a stretch,” Genovese admits. “Yet, what sets us apart is our holistic offering – a blend of unique products, solutions, and services, all tailored to meet individual customer needs.”
“The real metric is the efficacy. Without our technology, haulers are leaving money on the table every single day. Traditional tracking methods just aren’t cutting it.” Genovese cites a recent study on overflow detection as a case in point. When drivers were incentivized to report overflowing containers, they managed to identify just 13 in a month. In stark contrast, WasteVision AI’s system pinpointed a staggering 320.
The collaboration with Keymakr isn’t incidental. Following the recent launch of Keylabs, Keymakr’s data annotation SaaS platform, the partnership holds promising prospects. Keylabs empowers companies, even those without in-house data annotation teams, to harness Keymakr’s unparalleled annotation prowess. The platform boasts an intuitive interface, accelerating annotation and ensuring unparalleled accuracy.
“This isn’t uncharted territory for us,” reflected Arie Zilberman, CEO and founder of Keymakr, on their venture into the waste management sphere. “Owing to our expertise and the burgeoning demand, we’ve curated a dedicated team of expert annotators specialized in waste management. No training required; they’re masters in garbage classification. This proficiency means we not only save time but also reduce costs for significant projects. As machine learning permeates every sector, our goal is to equip businesses with the resources they need to upscale data annotation and craft their models with both precision and efficiency.”
