We study a two-player zero-sum inspection game with incomplete information, where an inspector deploys resources to maximize the expected damage value of detected illegal items hidden by an adversary across capacitated locations. Inspection and illegal resources differ in their detection capabilities and damage values. Both players face uncertainty regarding each other’s available resources, modeled via stochastic player types. To solve this large-scale game, we first characterize the locations’ marginal detection probabilities and expected damage values in equilibrium. We then design combinatorial algorithms that coordinate each player type’s resources to match these marginal values while satisfying best-response conditions. Our approach computes a Nash equilibrium with linear support in polynomial time. Notably, equilibrium strategies are independent of the inspector’s detection capabilities, implying no strategic advantage from concealing them. We extend our analysis to a two-stage problem, computing in pseudo-polynomial time the optimal acquisition of inspection resources given subsequent interactions with the adversary. A case study on drug interdiction at U.S. seaports shows that reducing uncertainty about drug shipments enables the inspector to detect an additional $20.04 million worth of narcotics annually, quantifying the value of intelligence. Our results provide actionable guidance for security agencies acquiring and deploying heterogeneous inspection resources under uncertainty.