Computer vision (CV) technology is revolutionizing many industries, including healthcare, retail, automotive, etc. As more companies invest in computer vision solutions, the global market will multiply 9 times by 2026 to $2.4 billion.
However, implementing computer vision in your business can be challenging and expensive, and improper preparation can lead to CV and AI project failure. Therefore, business managers need to be careful before initiating computer vision projects.
See the top 7 challenges that business managers can face while implementing computer vision in their business and how to overcome them to safeguard their investments and ensure maximum ROI. We also provide some examples in the recommendation sections
1. Data quality
You can work with an image data collection service to help you obtain high-quality visual datasets for your computer vision project.
Poor quality
High-quality labeled and annotated datasets are the foundation of a successful computer vision system.
In data-sensitive industries like healthcare, where computer vision is widely used, accurate data annotation and labeling are essential, as errors can have serious consequences. For example, many tools built to catch COVID-19 failed due to poor data quality.
Recommendations
Working with medical data annotation specialists can help mitigate this issue.
You can check our list of medical data annotation tools to choose the option that best suits your healthcare computer vision project needs.
Lack of training data
Collecting relevant and sufficient data can have various challenges. These challenges can lead to a lack of training data for computer vision systems.
For example, gathering medical data is a challenge for data annotators. This is mainly due to the sensitivity and privacy aspects of healthcare data.
Most medical images are either sensitive or strictly private and are not shared by healthcare professionals and hospitals. Additionally, it is possible that the developers do not have the resources to collect sufficient data.
Recommendations
To ensure that you have adequate data to train your computer vision system, leverage outsourcing or crowdsourcing. This way, the burden of collecting data and ensuring its quality will be transferred to a third-party specialist, and you can focus on developing the computer vision model. You can also work with a video data collection service to obtain high-quality visual datasets for your CV project.
2. Inadequate hardware
Computer vision technology is implemented with a combination of software and hardware. To ensure the system’s effectiveness, a business must install high-resolution cameras, sensors, and bots. This hardware can be costly and, if suboptimal or improperly installed, can lead to blind spots and ineffective CV systems.
IoT-enabled sensors are also required in some CV systems; for example, a study presents the use of IoT-enabled flood monitoring sensors.
Recommendations
The following factors can be considered for effective CV hardware installation:
- The cameras are high-definition and provide the required frames per second (FPS) rate
- Cameras and sensors cover all surveillance areas
- The positioning covers all the objects of interest. For example, in a retail store, the camera should cover all the products on the shelf.
- All the devices are properly configured to avoid blind spots.
One good example of improper hardware for CV is Walmart’s shelf-scanning robots. Walmart recalled its shelf-scanning robots and finished the contract with the provider. Even though the CV system in the bots was working fine, the company found that customers might find them strange due to their size, and they found other, more efficient ways.
On the other hand, Walmart-owned retail brand Sam’s Club mounted new CV-enabled inventory scanning systems, made by Brain Corp, on its already operating autonomous floor cleaning robots. Sam’s club finds them more effective and plans to increase the investment.
Another example is Noisy student, which is a semi-supervised learning approach developed by Google that relies on convolutional neural networks (CNN) and 480 million parameters. Processes like these require heavy computer processing power.
Two of the most significant costs to consider before starting your computer vision project are:
- The hardware requirements of the project
- The costs of cloud computing
3. Weak planning for model development
Another challenge is weak planning when creating the ML model deployed for the computer vision system. During the planning stage, executives tend to set overly ambitious targets, which are hard for the data science team to achieve.
Due to this, the business model:
- Does not meet business objectives
- Demands unrealistic computing power
- Becomes too costly
- Delivers insufficient accuracy and performance
Recommendations
To overcome such issues, it is important for business leaders to focus on:
- Creating a strong project plan by analyzing the business’s technological maturity levels
- Create a clear scope of the project with set objectives
- The ability to gather relevant data, purchase labeled datasets, or gather synthetic data
- Consider the model training and deployment costs
- Examining existing success stories similar to your business.
4. Time shortage
During the planning phase of a computer vision project, business managers tend to focus overly on the model development stage. They fail to consider the extra time needed for:
- Setup, configuration, and calibration of the hardware, including cameras and sensors
- Collecting, cleaning, and labeling data
- Training and testing of the model
Failure to consider these tasks can create challenges and project delays
A study of companies developing AI models found that many companies have exceeded the expected time for successful deployment.
Another recent study identified that 99% of computer vision project teams faced significant delays due to a multitude of reasons
Recommendations
We recommend performing early calculations of each stage of the development process. If the project is time-constrained, then certain tasks, such as algorithm development or data collection, can be outsourced.
5. Vulnerability to Adversarial Attacks
Computer vision models are susceptible to adversarial attacks, where malicious actors manipulate input data to deceive the system. For example, imperceptible changes to an image (like subtle noise patterns) can cause a model to misclassify objects.
Recommendations
- Invest in adversarial training, where models are trained on perturbed data to improve robustness.
- Implement input sanitization techniques to detect and filter out adversarial examples.
- Collaborate with cybersecurity experts to audit and harden your CV systems against attacks.
One-Pixel Attack
The one-pixel attack exploits neural networks’ sensitivity to minute input changes. Researchers demonstrated that altering a single pixel in an image can cause models to misclassify objects with high confidence. This vulnerability undermines trust in CV systems for applications like medical imaging or quality control, where precision is critical.
Example: In a lab setting, a one-pixel attack caused a pneumonia detection model to misdiagnose X-rays, highlighting risks in healthcare AI.
Recommendations
- Employ defensive techniques like gradient masking or input normalization to reduce sensitivity.
- Regularly update models with patches to address newly discovered attack vectors.
- Conduct third-party penetration testing to identify vulnerabilities.
6. Natural Distribution Shifts
Computer vision models often struggle with distribution shift scenarios where real-world data differs from training data. For instance, a model trained on daylight images may fail in low-light conditions, or a system optimized for specific camera angles might misbehave when deployed in a new environment. This is particularly problematic in industries like agriculture or automotive, where lighting, weather, and sensor variations are common.
Example: Tesla’s Autopilot system faced scrutiny when its cameras misinterpreted bright sunlight as a yellow traffic light, highlighting the risks of distribution shifts.
Recommendations
- Use data augmentation to simulate diverse conditions (e.g., lighting changes, occlusions) during training.
- Continuously test models on edge cases and real-world scenarios.
- Partner with domain experts to identify potential shifts and refine training datasets.
7. Detail Reduction in Small Objects
Computer vision systems often struggle to detect or classify small objects in images due to limited pixel information and the inherent design of convolutional neural networks (CNNs). When objects occupy fewer pixels (e.g., tiny tumors in X-rays, distant vehicles in surveillance footage), CNNs lose critical details during downsampling operations like pooling or strided convolutions. This reduces accuracy in applications with critical small-object detection, such as medical imaging, satellite analysis, or industrial quality control.
Recommendations
- Sensor and resolution upgrades: Use high-resolution cameras or sensors to capture finer details of small objects.
- Architectural adjustments: Avoid aggressive downsampling by reducing pooling layers or dilating convolutions.
- Data augmentation: Generate synthetic training data with small objects in varied sizes/positions to improve model sensitivity.
- Post-processing: Apply super-resolution techniques or edge-enhancement algorithms to recover lost details in input images.
You can also check out our sortable and filterable lists of services, vendors, and tools to choose the option that best suits your business needs:
- Data Annotation / Labelling / Tagging / Classification Service
- Video Annotation Tools
- Medical Image Annotation Tools
- Image Recognition Tools
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