Computer vision, a rapidly growing technology, continually discovers new applications across various industries. It offers automation, cost-saving, and the potential to enhance products and processes, making it a sought-after tool for many companies. However, implementing computer vision projects involves complexities, starting from algorithm development and hardware requirements to setup and maintenance. These challenges are pivotal factors determining the success of such initiatives!
Unfortunately, many projects encounter failure, either never reaching production or exceeding deadlines and budgets without demonstrating a return on investment (ROI). Inexperienced teams often fall into common pitfalls, such as overly complex scopes, unreliable data, or setting unrealistic and unmeasurable goals.
To mitigate the risk of failure, teams are advised to adhere to established best practices crafted by industry experts. At BoBox, we’ve contributed to multiple computer vision projects, developing standards that guide the definition, execution, and monitoring of project progress from initial design to deployment, production, and maintenance.
Even the most innovative ideas can fail if not described and defined properly. It is important to provide a clear business understanding of the goal and measurable expected outcomes. In the case of Computer Vision one of the essential aspects is the expected area of application – the environment conditions and scenery in which the solution will operate. This also includes the hardware requirements and limitations – both in terms of image acquisition part (cameras) as well as computing units specification (available processing power). In this step it is also recommended to assess legal aspects of data ownership and availability.
Some basic understanding of typical computer vision techniques will definitely help to define the features of the designed computer vision system and choose adequate algorithms. This step includes the decision on CV methodology (such as object detection, segmentation, keypoint detection – read more on our blog), choice of features to be recognized on the image stream, specifying training dataset standard (quality, diversity, size), model output and output post-processing steps.
BoBox experts are ready to help you to translate your business requirements to these technical features.
Once the dataset specification is defined, data can be collected and preprocessed accordingly. A good quality dataset is a key to success. Careful data selection and quality control should not be underestimated – read more on dataset importance on our blog. Make sure to get early data samples to work on the data preparation guidelines with the team. Computer vision algorithms such as object detection and segmentation require labeled data. Depending on your use case, the project may utilize the existing open datasets or you will need to create your own. In such cases you need to agree on precise rules for data labeling to ensure data consistency. Usually it takes a few iterations to come up with a set of guidelines that allow for consistent image annotation.
Image or video annotation involves human-powered work defined as the task of annotating an image or video with labels. It is a time consuming part, and the quality of work determines to great extent the performance of the trained model. Make sure you have a well trained team, revise the work and re-iterate on discovered edge cases. If you do not have nor intend to build your own annotation team, you may consider outsourcing annotation. BoBox can provide you with an experienced team led by a manager that facilitates the annotation and data handover process. Read more here how we deliver value to your CV projects.
Once you have first annotated data, start training. It is recommended to try different models, compare the results, see how they process your specific data, and select one to be developed. At this stage, it is important to set up model evaluation metrics to be able to compare and observe improvement in time. Re-iterate as the annotated dataset increases, observe any biases and malfunction (e.g. false positives) to provide recommendation for further dataset extension. Finally, test the model in your business environment and against business end metrics as early as possible, as the technical metrics do not guarantee the success. Also, even after deployment to production, be prepared to monitor and evaluate the model on a regular basis. BoBox experts can perform regular inspections of model output to provide recommendations on model maintenance steps.
In the fast-paced realm of technology, computer vision emerges as a game-changer, offering limitless possibilities across industries. Yet, navigating its complexities demands adherence to proven strategies. BoBox shares insights gleaned from extensive experience, outlining key steps from scope definition to model evaluation. By embracing these best practices, organizations can confidently embark on successful computer vision projects, harnessing its transformative power to drive innovation and efficiency.
