Artificial intelligence, at present, borders on the edge of crazy hype. While there are some playing the waiting game to see how AI can help their industry, its impact on construction sites is clear: AI, when used properly, prevents crime and vandalism on jobsites.
Security when it comes to these sites runs the gamut, from live guards, fences, as well as other protective provisions on one end to nothing when it comes to the other end. However, securing a jobsite happens to often be a challenge when it comes to construction companies, as the cost is often either passed on to the customer with higher prices or else eaten by the company altogether. Construction sites can indeed be easy pickings for criminals, as they often contain expensive materials that happen to be lightly guarded, just the way what criminals are looking for them. This is one of the major reasons why the National Insurance Crime Bureau goes on to report that theft costs construction companies almost $1 billion every year.
AI-enabled security surveillance can go on to make sites more secure. This can add up to massive dollars as compared to the costs that are associated with stolen tools, appliances, lumber, heavy equipment, and even other valuables on the jobsite.
There happen to be a number of things construction companies can go on to do in order to reduce crime on jobsites. However, the reason why AI-based video surveillance goes on to work well is that it helps valuable items be seen at all times sans requiring the cost as well as management headaches of on-site personnel. When something odd happens to be in the process of happening, AI can very rapidly alert human monitors so that they can take apt countermeasures. And unlike humans, AI can always be expected to not get distracted, get tired, or even forget what it has learned. This goes on to mean that it can look at video footage all day long and even be alert to anomalies that it witnesses.
Getting Smarter With Time
What most people don’t go on to realize is that AI happens to get smarter as time passes, learning from the specific environment so as to be more effective. The more it looks at something, the better it goes on to get, like someone who recalls something after getting it to experience it once. For construction companies, every site happens to be different, so this is very crucial. Well-trained AI will gauge when someone happens to be on site who should not be there or when the intruder happens to be a person and not an animal, and thereby will alert on it in no time.
AI video happens to be usually made available by way of mobile video surveillance units that do not require any sort of hard wiring for electricity or even internet connectivity, which happens to be the state of most of the jobsites. These autonomous units can go on to watch valuable material as well as alert remote guards and site owners when something that should not be taking place is happening. These units can also make use of audio and visual deterrents so as to scare off would-be criminals, and if everything else fails, remote guards can go on to contact local authorities in order to intervene if something criminal is taking place.
Enter the Era of AI
Some companies have gone on to procure video monitoring equipment in their effort to decrease crime on jobsites. Over the years, this tech has evolved in three distinct ways. Early generations, when it comes to surveillance equipment, were purely recorded and store techs that could provide after-the-fact evidence of crime taking place on the jobsite. Although this was indeed useful for making insurance claims and also providing evidence to law enforcement, it happened to be passive in nature and did not happen to do much, if anything, so as to prevent theft as well as vandalism.
The next step happened to be remote video monitoring- RVM services which helped the construction companies work with third parties so as to provide 24*7 active monitoring. This brings one to the next evolution of video surveillance, which is using AI so as to analyze video in real time. Instead of relying completely on humans watching hours or days when it comes to video feeds so as to detect suspicious events, AI can go on to detect these events automatically and at the same time alert a person for more review. This happens to be a real-world instance of humans working in sync with AI so as to become better at their jobs. However, for AI to be precisely accurate and efficient when it comes to this scenario, it has to be trained on specific job sites so that it can enable telling the difference between something innocent and something criminal.
This site-specific fundamental to AI is important, since if it is not trained the way it should be, AI video can go on to generate a profusion of false positive alerts and also send people down rabbit holes that go on to defeat the whole reason for making use of AI in the first place, thereby enhancing  the accuracy and efficacy of the surveillance.
No two sites happen to be alike
Each jobsite happens to be unique – some happen to be in the country, whereas others are in the city, some near a busy road, while some aren’t. This is where AI needs to be tuned to its specific location. For instance, if the jobsite happens to be located in woodlands, the AI requires to understand the difference between wildlife and people, or else it might as well as issue an alert every time a raccoon stumbles on the property. Moreover, AI has to understand the difference between innocent and criminal behavior is the actor happens to be on foot or in a vehicle, stopping, or otherwise, thereby acting in a manner that is suspicious or just passing by.
Machine learning, which is a component of AI, happens to be the method for fine tuning as well as making AI smarter. The more experience AI gains watching this activity, the more precise and efficient it goes on to become in distinguishing activities that happen to be actionable and those that are not. It goes on to stand for a reason: a system that processes activity, in both innocent as well as criminal form, many times will be more accurate than a system looking at an activity taking place for the first time.
This is where AI should be especially trained for each jobsite, when it comes to the uniqueness of their locations, threats they face, as well as the sources for false positives. This training can be done before the system goes live, having data from similar locations being run by way of AI, so it gets properly trained right from the start.
The more an AI video surveillance system can get fine-tuned to a unique jobsite, the faster and more accurately the system can thwart criminal activity. There happens to be no such thing as one-size-fits-all when it comes to AI, and when done the way it should be, customizing as well as training AI for individual construction jobsites can happen to be a powerful tool in terms of crime deterrence.
