To tackle the challenge of segmenting smalldebris targets against complex backgrounds in static explosion imagery, we've refined a multi-threshold segmentation technique using a tasmanian devil algorithm. This method leverages Tent chaos mapping for population initialization and an adaptive weight strategy to bolster global search efficiency. Italso integrates an elite reverse learning strategy to evade local optima traps. Using ITDO to solve for the minimum value ofTsallisrelativeentropy asthe targetfunction valueto calculatethe optimalthresholdfordebrisimage segmentation. Simulations reveal that the ITDO algorithm outperforms others in convergence and stability across 12 benchmarks. The ITDO-Tsallis algorithm , notably, offers swifter convergence and more precise target resolution than its counterparts, proving its efficacyin debris image segmentation within static explosion fields.