In the computer vision task of semantic segmentation, each pixel in an image is classified into a particular class or category. Unlike object detection, which detects objects and provides bounding boxes, semantic segmentation assigns a class label to each individual pixel. This results in a pixel-wise classification map that identifies different objects or regions within an image. In the present study, we have tried to compare two very popular techniques used for detecting the object in an image using computer vision. Semantic segmentation and bounding box algorithms have been compared in a controlled environment, keeping the dataset, train-test split, batch size, training epochs, and other factors constant. We have compared these 2 techniques for loss minimization and area of overlap functions. A gradual decrease in the training loss has been observed in semantic segmentation technique, while the bounding box algorithms generate a steep decrease specifically in the later epochs. Choosing appropriate object detection techniques should address the problem of small regions of interest (ROI) compared to the total image area of class imbalance problem in semantic segmentation.