The mining industry is a perilous environment for its workers. Inmany such locations, dynamite is employed for mine blasting to expedite operations. For instance, if a robot were equipped with a camera instead of a human, and this robot could autonomously identify unexploded dynamite, it could significantly enhance the safetyof mine workers following an explosion. This system would necessitate object detection in conjunction with the robot’s visual perception. This report seeks to address the question: ”Can a ConvolutionalNeural Network (CNN), such as YOLOv8, still accurately predictreal objects when trained solely with semi-synthetic images?” Themethod for this work included detection using a color range on anedge device followed by (to double check) detection using AI withYOLOv8, which was trained with only real background images with3d CAD images of a dynamite using the program Blender pasted ontop. Stage 2 (YOLOv8) after being trained with only semi-syntheticimages could predict with approximately 99.1% accuracy in imagesof plastic dynamite as having a dynamite. Stage 2, despite trainedwith much less epochs than recommended (10 instead of 100) was accurate when being tested on real images with a similar environment(beach) as the semi-synthetic images but not so accurate when testedon real images of a dynamite and a similar object in environmentsnot similar to the training images, in those images there where stillmany detections but mostly with many smaller bounding boxes onthe same dynamite instead of one big bounding box.