Authors - V V N Sai Rajeshwar, Gangalam Sumanth Abstract - Attributes like size, shape, color, quantity, quality, etc. are very hard to find in an image of a particular object. There are various ways to process an image and derive attributes namely – Faster R – CNN, SSD (Single Shot Detector), SPP (Spatial Pyramid Pooling) - Net. Many modern technologies use YOLOv8, the 8th version of YOLO (You Only Look Once), which is quite accurate and detects real - time objects. To enhance the proficiency and scalability of the model Deep Convolutional Neural Networks (DCNN) were used. This paper highlights a very unique strategy to enhance the abilities of YOLO with the help of dynamic masking and also advances the searching and findings of attributes in a unique way. The use of Compute Unified Device Architecture (CUDA) which uses GPUs rather than CPU, made it convenient to run on a higher loads of data. After masking an image or stream of images, detecting colors is easier, and expressing dominant colors is the motive of this research. Availing Coordinates of specified images in a particular image or stream of images is very helpful in various surveillance-related actions like military, and navy, and also useful in domestic purposes.
