An Intelligent Target Detection and Recognition Technology Based on DS Evidence Theory and Information Fusion in the Optical-Electronic Domain

To address the challenges in detection and recognition arising from significant variations in target pixel occupancy within the field of view, an optoelectronic intelligent perception framework based on Dempster-Shafer (DS) evidence theory is proposed in this paper. The implementation of this framework proceeds in two primary stages. First, a multi-dimensional parallel detection mechanism is established to simultaneously execute four heterogeneous algorithms on single-frame imagery. These algorithms encompass prior-knowledge-based and deep-learning-based detection for small targets, large-scale target confirmation, and fine-grained classification, thereby comprehensively capturing the spatiotemporal positions and attribute features of the targets. Subsequently, DS evidence theory is employed as a decision-level fusion strategy to synthesize the aforementioned multi-source heterogeneous evidence, ultimately yielding high-confidence integrated recognition results. Simulation results demonstrate that the target detection and recognition technology achieves a detection probability of 98.5%, a false alarm rate of 1.8%, and a coarse-level classification accuracy of 98%. This technical solution holds significant practical value and offers broad reference significance fir systematically addressing detection and recognition functions in optoeletronic equipment.