Unexploded　ordnance　(UXO)　presents　a　significant　risk　to　both　the　natural　environment　and　human　safety.　Current　deep　learning　detection　mechanisms　are　characterized　by　limited　interpretability,　resulting　in　a　persistent　absence　of　detection　methods　that　are　simultaneously　efficient,　secure,　and　precise.　To　address　these　challenges,　a　dual-mode　detection　method　based　on　interpretation-promoted　YOLOv5　is　proposed　in　this　paper.　A　comprehensive　dataset　has　been　constructed　to　focus　on　the　scarcity　of　low-altitude　UXO　target　datasets,　incorporating　both　visible　light　and　infrared　imagery.　Dataset　augmentation　has　been　implemented　through　the　application　of　generative　adversarial　networks　for　image　super-resolution　reconstruction,　thereby　enhancing　the　robustness　of　the　dataset.　To　tackle　issues　of　low　detection　accuracy　and　inadequate　background　discrimination　associated　with　a　single　information　source,　an　integration　of　visible　light　and　infrared　data　has　been　proposed　to　enhance　the　interpretability　of　the　YOLOv5　algorithm,　leading　to　improved　detection　performance.　Extensive　low-altitude　detection　experiments　were　conducted　in　field　environments　using　UAVs.　Experimental　results　demonstrate　that　the　proposed　method　achieves　a　remarkable　detection　accuracy　of　up　to　97.1%　and　an　impressive　detection　speed　of　up　to　60.3　frames　per　second.　　©　2013　IEEE.