Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and overlapping kitchen utensils from internet were used as base benchmark objects. The evaluation and training/validation sets are set at 20% and 80% respectively. This project evaluated the performance of these techniques and analyzed their strengths and speeds based on accuracy, precision and F1 score. The analysis results in this project concluded that the YOLOv5 produces accurate bounding boxes whereas the Faster R-CNN detects more objects. In an identical testing environment, YOLOv5 shows the better performance than Faster R-CNN algorithm. After running in the same environment, this project gained the accuracy of 0.8912(89.12%) for YOLOv5 and 0.8392 (83.92%) for Faster R-CNN, while the loss value was 0.1852 for YOLOv5 and 0.2166 for Faster R-CNN. The comparison of these two methods is most current and never been applied in overlapping objects, especially kitchen utensils.