Aiming　at　failure　detection　problems　on　subtle　region　caused　by　saliency　differences　of　detected　target　in　local　region,　under　the　framework　of　Bayesian　theory,　the　author　proposed　a　novel　salient　region　detection　method　based　on　cellular　automata　multi-scale　optimization.　Firstly,　the　prior　information　about　dark　channel　was　integrated　with　regional　contrast　to　separately　construct　original　salient　maps　in　five　superpixel　scale　spaces　on　the　same　picture;　and　then　the　cellular　automata　was　used　to　establish　a　dynamic　updating　mechanism　and　impact　factor　matrix　and　confidence　matrix　were　applied　to　optimize　influences　of　each　cellular　in　next　state.　As　a　result,　the　saliency　values　of　all　cells　will　be　renovated　simultaneously　according　to　the　proposed　updating　rule,　and　five　optimized　salient　maps　were　obtained;　finally,　under　the　framework　of　fusion　algorithm　in　Bayesian　theory,　the　final　saliency　map　was　obtained.　The　experiment　on　two　standard　image　datasets　with　different　complexity　was　conducted,　and　experimental　result　indicates　that　the　performance　of　proposed　algorithm　is　superior　to　other　ten　existing　salient　region　detection　algorithms　both　in　visual　effect　and　in　objective　quantitative　comparison.　Especially　on　the　most　challenging　DUT-OMRON　data　base,　the　aggregative　indicator　F-measure　value　of　proposed　algorithm　is　0.631　4,　and　mean　absolute　error　(MAE)　is　0.132　5　and　ROC　area　under　the　curve　(AUC)　is　0.892　8,　indicating　that　the　algorithm　has　higher　accuracy　and　robustness.　©　2017,　Science　Press.　All　right　reserved.