This challenge is open to sole innovators, industry, academic and research organisations of all types and sizes. There is no requirement for security clearances.

Solution providers or direct collaboration from countries listed by the UK government under trade sanctions and/or arms embargoes are not eligible for HMGCC CoCreation challenges.

Bomb disposal is dangerous work undertaken by military and policing agencies among other government organisations. A key part of the process to safely disable a suspected explosive device while limiting physical contact, is using x-ray images to identify the electronic components that make up the switching mechanism designed to detonate a suspected explosive device.

This task can be further complicated by the presence of tripwires, anti-tamper mechanisms, and other countermeasures that may trigger the device if disturbed.

The gap

Analysing x-ray images of explosive devices is a highly specialised skill. Because specialists are not usually deployed in the field, operators on the ground must capture images and send them to remote experts for analysis and advice. This process can be slowed down by poor-quality images taken under operational pressure, the need for additional imaging, and the time it takes to send large files
over limited communications links.

Advances in artificial intelligence and machine learning, used to identify key electronic components in complicated circuits, could bring some of this analysis closer to the point of need. This could help operators identify and classify electronic components more quickly in the field, while ensuring human operators remain responsible for all operational decisions.

Sergeant Heyland is a member of a specialised Explosive Ordnance Disposal (EOD) team deployed overseas.

While on patrol, he finds a suspected Improvised Explosive Device (IED). It looks complex, so the analysis is likely to be difficult. After securing the area, he takes xray images of the device from multiple angles to gather as much information as possible, then withdraws to a safe distance.

Using a newly deployed analysis tool, Sergeant Heyland processes the images himself, onsite. The software identifies and labels electronic components within the images, flags anything it is uncertain about and asks for further images to improve confidence in the assessment.

Once Sergeant Heyland provides these further images, the system identifies a range of electronic components and provides assesses their likely characteristics and functions and highlights any that may be critical to how the device works.

As this is safety-critical work, Sergeant Heyland still consults UK-based specialists. However, he can now send smaller, more focused information, so his questions are answered more quickly.

This means Sergeant Heyland can complete his task more efficiently, while specialists continue to provide expert oversight.

This challenge is about developing machine learning algorithms that can identify and label electronic components from x-ray images while assessing their likely function within a circuit.

Applicants should aim to deliver a demonstrator at a minimum of Technology Readiness Level (TRL) 5 (technology validated in a relevant environment) within the 12-week project period.

Essential Requirements

• Deliver a demonstration to the sponsors, along with the software, source code and a report detailing how it functions.
• Use machine learning techniques to identify and analyse electronic components
• Ensure the software can be used by trained operators who are not electronic component specialists
• Provide traceability and explainability for outputs.
• Show confidence scores for identified components and extracted information.
• Run on commercially available hardware.
• Support multiple data types, particularly TIFF and JPEG files
• Demonstrate identification and analysis of through-hole components, such as capacitors and resistors.

Desirable Requirements

• Support offline or disconnected operation.
• Support retraining or refinement using additional labelled data.
• Demonstrate identification and analysis of surface mount components, such as microcontrollers, capacitors and resistors.
• Generate structured reports suitable for technical assessment activities.

Constraints

• Training data will be made available to the winning solution provider.
• Solutions should avoid reliance on proprietary datasets where possible.
• The solution must use x-ray images as its primary source of analysis.
• Solutions should provide evidence-backed outputs and clearly explain any uncertainty

Not required: horizon scanning.

Competition opens: Monday 28 September 2026

Clarifying questions deadline: Friday 16 October 2026 (clarifying questions can be submitted directly to cocreation@hmgcc.gov.uk before the deadline with the challenge title as the subject)

Clarifying questions published: Thursday 22 October 2026

Competition closes: Thursday 29 October 2026

Applicants notified: Tuesday 10 November 2026

Pitch Day: Thursday 19 November 2026

Pitch Day outcome: Monday 23 November 2026

Commercial onboarding begins: Friday 27 November 2026 (the successful solution provider will be expected to have availability for a one-hour onboarding call via MS Teams on the date specified to begin the onboarding/contractual process)

Target project kick-off: December 2026