Bruising　is　one　of　the　key　factors　that　causes　postharvest　losses,　which　decreases　the　economic　efficiency　of　fruit.　Nevertheless,　the　detection　of　bruises　still　relies　mainly　on　manual　work,　which　is　strongly　subjective　with　long　labor　time　and　low　efficiency.　Accordingly,　it　is　necessary　to　design　an　efficient　fruit　bruise　detection　approach.　Thermal　imaging　(TI)　is　a　fast　and　effective　nondestructive　testing　technology.　However,　the　commonly　applied　thermal　excitation　TI-based　bruise　detection　may　lead　to　a　decrease　in　the　shelf　life　of　the　fruit.　Therefore,　this　study　uses　apple　as　the　research　object,　introduces　cold　excitation　to　improve　the　sensitivity　of　bruise　detection,　and　then　constructs　a　simple　longwavelength　infrared　range　(7.5-13　mu　m)　TI　system　to　acquire　the　thermal　image　of　bruised　apples.　In　addition,　the　low　signal-to-noise　ratio　of　thermal　images　also　leads　to　detection　performance　degradation.　Thus,　the　YOLOv5s　network　is　applied　and　improved　to　achieve　better　detection.　The　specific　methods　are　described　as　follows:　(1)　Since　the　thermal　images　have　the　problem　of　duplicated　RGB　data,　group　convolution　is　used　to　reduce　the　feature　duplication　computation.　(2)　The　bottleneck　structure　of　YOLOv5s　is　replaced　by　the　ghost　bottleneck　(GB),　and　the　number　of　bottlenecks　is　reduced　to　decrease　the　computational　quantity　of　extracting　redundant　features　of　thermal　images.　(3)　The　shrinkage　module　is　inserted　into　the　GB,　and　the　threshold　is　automatically　obtained　through　two　fully　connected　layers　without　relevant　professional　knowledge　to　eliminate　noise　in　the　features　that　may　cause　performance　degradation.　The　F2　score,　mAP　and　mAP50　of　the　proposed　model　are　97.76%,　86.24%　and　98.08%,　respectively,　which　are　better　than　those　of　YOLOv5s.　Moreover,　the　computation　and　the　FPS　of　the　proposed　model　are　1.31　GFLOPs　and　160,　which　are　31.95%　and　121.21%　of　those　of　the　YOLOv5s,　respectively.