Panoramic　dental　X-ray　images　are　crucial　tools　in　dental　diagnosis,　and　accurate　detection　of　teeth　and　related　lesions　is　essential　for　clinical　decision-making.　However,　the　complexity　of　tooth　structures,　variability　in　image　quality,　and　scarcity　of　annotated　data　make　achieving　precise　automatic　target　detection　challenging　in　this　field.　In　this　study,　a　dental　X-ray　disease　target　detection　framework　based　on　feature　selection　and　enhanced　reuse　was　proposed　to　address　these　challenges.　An　improved　convolutional　neural　network　architecture　was　designed　and　implemented　by　combining　selective　receptive　field　fusion　with　shape-sensitive　multi-scale　feature　extraction　modules　to　enhance　the　ability　to　detect　targets　of　different　scales　and　shapes.　In　addition,　a　novel　feature　reuse　and　skip　fusion　technique　was　introduced　to　further　improve　the　utilization　of　features　by　the　backbone　network.　To　address　the　current　lack　of　annotation　for　impacted　teeth　positions,　an　impacted　tooth　location　image　dataset　named　DENIMPACT　is　also　presented,　which　significantly　addresses　the　shortcomings　of　current　deep　learning　object　detection　algorithms　in　the　clinical　diagnosis　of　dental　impacted　teeth　on　panoramic　X-rays.　Through　experiments　on　our　dataset,　our　method　achieved　a　significant　improvement　in　detection　accuracy,　with　the　mAP50　increasing　from　0.410　to　0.631.　The　experimental　results　demonstrate　that　our　model　achieves　state-of-the-art　performance　in　tooth　and　lesion　detection　tasks,　providing　new　solutions　for　the　automated　analysis　of　oral　medical　images.　The　dataset　will　be　released　at:　https://github.com/hexiaomo624/DENIMPACT.