AbstractIn 2011, 11.6 million people hunted large game in the United States. Hunting is a potentially dangerous sport, with 530 firearm fatalities in 2013 (National Safety Council, 2015). One potential solution to reduce firearm injuries is the development of “smart guns.” Generally, a smart gun is defined as a firearm that can be only be shot by an authorized user. The Raspberry Pi is an small, inexpensive microcomputer that commonly runs Python programs. First, a program was written in Python to capture images from a webcam. The program individually analyzed each pixel in a captured image and determine its color. For pixels matching RGB #FF6700, commonly known as “Safety Orange” (ColorHexa, 2018), the program kept count of those pixels. Next, targets of various sizes were constructed and covered with safety orange fabric. A Raspberry Pi running the developed program was set up in a fixed location and the each target placed one, two, and then every five meters from the lens of the camera. The program was run for 100+ frames and the number of successful detections was collected at each distance. This hypothesis was supported. Colored targets of different sizes were detected more accurately (more pixels detected) when closer to the apparatus. Beyond five meters, target size did not significantly affect the number of pixels detected. Light is governed by the inverse square law, which dictates that intensity of reflected light from the target (and pixel counts) decreases rapidly as distance increases (Nave, 2018).