Computer vision for forensic science: Detecting hidden clues

Investigations depend heavily on physical evidence collected at crime scenes, and a lot of that evidence is visual. Traditionally, gathering these clues has been manual - photographing the scene, sketching layouts, labeling objects, and relying on the investigator’s trained eye to spot subtle details.

Over the years, tools like thermal cameras and fingerprint scanners have supported this work and played a key role in solving crimes. But now, thanks to AI and computer vision, investigations are becoming more advanced. Machines can assist in analyzing visual evidence faster, more accurately, and at a much larger scale.

In particular, computer vision is an impactful technology that enables machines to see and understand images and videos. These systems can quickly process and analyze large amounts of visual data, helping investigators solve crimes faster.

The advantages of AI-driven tools in investigations have pushed the global market for AI-driven digital forensics tools to be valued at $4.98 billion in 2025 and is expected to reach $17.7 billion by 2031. 

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In this article, we’ll explore how computer vision can be used in forensic investigations and how it can help identify, analyze, and interpret visual evidence.

The role of AI and computer vision in forensics

Computer vision enables machines to interpret visual information from the real world and make decisions based on that understanding. Specifically, in forensic science, AI can be used to detect elements like weapons or injuries, track vehicles, and even reconstruct 3D models of crime scenes with impressive speed and accuracy. 

While forensic investigations still rely on manual observation and expert knowledge, this process can be time-consuming and typically focused on one case at a time. Computer vision models like Ultralytics YOLO11 offer a faster, more scalable approach. These models can scan scenes, identify key objects, and classify evidence using Vision AI tasks like object detection and image classification.

For example, instead of manually reviewing hundreds of hours of surveillance footage, a computer vision model can automatically scan video feeds to detect suspicious activity - like unusual movements, loitering, or objects being left behind. It can flag specific timeframes and locations for investigators to review, significantly reducing the time and effort needed to find relevant evidence.

Applications of Vision AI in forensics 

Computer vision is changing the way investigators collect and study visual evidence. Here’s a closer look at how it’s being used in forensic work today.

Analyzing surveillance footage using computer vision

One of the most common computer vision techniques used in surveillance is object tracking - following people, vehicles, or items across video frames. Instead of being manually programmed for every situation, these systems learn from real-world video and quickly pick up what “normal” looks like. That way, they can automatically spot unusual behavior, like someone loitering in a quiet area, a vehicle in a restricted zone, or an unattended bag in a public space.

This tech can be useful during investigations. For example, if a witness says they saw a red pickup truck at an intersection between 11 a.m. and 1 p.m., computer vision can scan hours of CCTV footage, pick out every instance of a red truck, and save investigators from reviewing it all manually. By automating what used to take hours or days, computer vision makes both live monitoring and evidence review faster, smarter, and more effective.

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Using AI in bushfire investigations

In the aftermath of fire-related incidents, time is critical, and physical evidence is often compromised or destroyed. Computer vision technologies help fill this gap by analyzing diverse sources of visual data, such as satellite imagery, drone footage, CCTV, and thermal scans, to reconstruct the sequence of events.

These tools can detect the origin of a fire, identify potential accelerants, and track human or vehicle activity near the scene. When combined with criminal databases and incident reports, the system can uncover patterns, like repeated fire locations, suspect behavior, or coordinated arson, across different regions.

A real-world example comes from New South Wales, Australia, where police used a computer vision platform called Insights during investigations into the 2019–2020 bushfires. The platform analyzed CCTV footage, geospatial data, and environmental inputs such as wind direction and lightning strike records. It also used object recognition to identify relevant visual elements, such as vehicles or equipment, in vast amounts of surveillance footage.

By overlaying these insights with mobile phone geolocation data and maps, investigators could link human activity to specific fire events and visually present this data during formal inquests. This approach sped up the investigation process.

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Reconstruction of a crime scene using computer vision

3D reconstruction in forensics combines technologies like photography, laser scanning, LiDAR, and computer vision to create detailed, true-to-scale models of crime scenes. These reconstructions help investigators visualize object placement, blood spatter patterns, and bullet trajectories with a level of accuracy and interactivity that traditional 2D photographs can't provide. This technology enhances analysis and courtroom presentations by enabling virtual walkthroughs and precise measurements.

Beyond active investigations, 3D reconstruction and virtual reality (VR) are also reimagining forensic education. In academic settings, students can use VR headsets to explore simulated crime scenes, identify evidence, and practice techniques like fingerprint scanning and blood spatter analysis. This immersive learning approach builds core investigative skills in a safe, repeatable environment.

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Image enhancement using Vision AI

A 19-year-old case in Kerala, India, was recently solved with the help of AI-powered image enhancement. Investigators revisited old photographs from the time of the crime and used a combination of image processing and artificial intelligence to clarify facial features and generate updated likenesses of the suspects. One enhanced image closely resembled a person seen in a wedding photo shared online, which ultimately led to a breakthrough in the case.

This case is a good example of how modern forensic tools, particularly the integration of AI with traditional image enhancement, can help law enforcement revisit and resolve long-pending investigations. By revealing obscured details or identifying individuals who have changed significantly over time, these technologies are proving to be powerful assets in the field of digital forensics.

Using instance segmentation for evidence extraction

Instance segmentation is a computer vision task that can identify individual objects in an image and outline each one with a detailed, pixel-level mask. This helps investigators isolate key pieces of evidence with precision, even in messy or complex environments.

Let’s say there’s a crime scene where multiple sets of footprints or overlapping tire tracks are visible in dirt or snow - instance segmentation can distinguish and outline each one separately. This makes it easier to analyze movement patterns, vehicle paths, or suspect trajectories without distractions from background clutter.

Beyond scene analysis, instance segmentation is also being applied to forensic imagery. In a study by the Zurich Institute of Forensic Medicine, researchers used this technique to identify different types of wounds. They trained a vision model on 1,753 forensic images containing 4,666 wounds. 

The trained model was able to detect and classify seven types of injuries, including stab wounds and burns. It showcased how Vision AI can support investigators by quickly and accurately analyzing injury patterns, saving time, and improving consistency in forensic image assessments.

Automatic number plate recognition enabled by Vision AI

Automatic Number Plate Recognition (ANPR), or License Plate Recognition (LPR), is a computer vision application focused on automatically detecting, reading, and extracting license plate numbers from images or video footage. This typically involves using computer vision models, such as YOLO11, for object detection to locate the license plate within a frame. 

Once the plate is identified, optical character recognition (OCR) is applied to extract the alphanumeric characters. This information can then be cross-referenced with databases to identify the vehicle’s owner or track its past movements. ANPR is particularly useful in investigations involving missing persons, vehicle theft, traffic violations, or hit-and-run incidents.

For instance, in a missing person case, law enforcement can input the license plate number into an LPR system. If the vehicle appears on a surveillance camera, the system can automatically flag its location, helping officers respond quickly. LPR also plays a key role in cases like stolen vehicles or hit-and-runs by providing fast, accurate tracking through integrated surveillance networks.

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Pros and cons of using AI in forensics

AI and computer vision offer several key benefits that enhance the efficiency, accessibility, and reliability of forensic investigations. Here are some of the key advantages to consider:

• Remote analysis: Computer vision enables forensic investigators to view crime scenes and evidence, such as images, videos, and 3D models, from anywhere without being present in person.
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• Long-term archives: Data processed by vision systems can be well-organized and easily searchable. If needed in the future, it can be revisited and re-analyzed using improved models on older cases.
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• Consistency: Unlike humans, who may interpret visual information differently based on their experience, computer vision models apply the same rules every time, resulting in more consistent and uniform analysis.

Despite its potential, computer vision in forensics also comes with a few challenges to keep in mind. Here are some of the main ones:

• Input quality: Vision models rely heavily on the quality of the data they receive. Computer vision solutions may miss or misinterpret important details if images are blurry, poorly lit, or low-resolution. This can be mitigated by applying the correct data augmentation strategy. 
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• Difficulty in unseen data: Computer vision models can struggle when they encounter something new - like unusual objects or crime scenes that differ from the data they were trained on. This is known as “drift”.
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• Legal challenges: Using computer vision in investigations can raise legal questions, such as whether the evidence is admissible in court, how private the data is, and how transparent the system’s decisions are.

Key takeaways

Computer vision is still evolving, but it’s already transforming how forensic science operates. From detecting and outlining small pieces of evidence through instance segmentation to building 3D crime scene models and enhancing documents, it supports investigators at every stage of their work.

In the future, computer vision will likely work even more closely with AI and machine learning to spot patterns faster and make better predictions using past case data. As technology keeps improving, it’ll keep bringing new and better ways to solve crimes.

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