Expected Solution

Develop an AI-based mobile application that: Uses image processing to assess onion quality. Identifies damaged, rotten, sprouted, or undersized onions. Estimates Grade A and URS percentages. Generates a digital quality report instantly. Reduces human bias and improves transparency.

Background

Urban public transport buses traverse almost every major road in a city every day. Modern buses are increasingly equipped with multiple cameras covering the front, rear, sides, and passenger cabin. However, these cameras are primarily used for recording incidents and are not leveraged as intelligent sensing platforms. At the same time, city authorities rely on fixed CCTV cameras, manual inspections and citizen complaints to identify road defects, traffic congestion,missing infrastructure and unsafe driving behaviour. This results in delayed response,incomplete situational awareness and inefficient maintenance planning.

Description

Develop an AI-powered onboard and centralized software platform that transforms public transport buses into mobile urban sensing units. The onboard software shall analyse video streams from multiple bus-mounted cameras to detect road defects such as potholes, damaged roads, missing road dividers, missing zebra crossings, damaged or missing traffic signboards,waterlogging and other road hazards. It shall estimate vehicle density through vehicle detection, classification and counting, identify traffic bottlenecks, and detect vulnerable pedestrian situations such as school children crossing roads. During incidents such as hit-and-run or rash driving, the system should detect and track the offending vehicle, extract the registration number with a confidence score, timestamp and GPS location, and securely share alerts with a central command system. The centralized platform shall aggregate information from the entire bus fleet, visualize events on a GIS map, generate congestion heat maps,identify infrastructure deficiencies, analyse origin–destination traffic patterns, estimate route delays and provide actionable insights for transport authorities.

Expected Solution

The solution should provide an edge-AI onboard processing framework integrated with a centralized urban intelligence platform. It should generate reliable alerts, GIS-based dashboards, road condition maps, traffic analytics and incident reports to support proactive road maintenance, improved traffic management, enhanced public safety and evidence-based decision making while minimizing bandwidth through intelligent edge processing.

Background

Modern urban transportation networks face persistent challenges of traffic congestion, inefficient route planning, and high operational costs. Classical optimization techniques struggle with large-scale Vehicle Routing Problems (VRP) because of their NP-hard nature. While quantum computers offer theoretical advantages for combinatorial optimization,current hardware limitations prevent their direct large-scale use. Quantum-inspired metaheuristic algorithms (e.g., Quantum Particle Swarm Optimization QPSO) embed quantum-mechanical concepts into classical computation, delivering stronger global search, faster convergence, and a better balance between exploration and exploitation.

Problem Description

Develop a quantum-inspired metaheuristic optimization framework that dynamically generates near-optimal vehicle routes under real-time or simulated traffic conditions.The transportation network will be modelled as a weighted graph. The framework will focus on algorithms such as Quantum Particle Swarm Optimization (QPSO) and will be benchmarked against conventional metaheuristics and exact methods.

Objectives

Expected Solution

A complete software platform that implements a Quantum-Inspired Metaheuristic Optimization Algorithm for intelligent traffic routing. The platform must include graph-based network modelling, mathematical formulation of the optimization problem,constraint handling, convergence analysis, and systematic performance benchmarking.

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