This study addresses the problems of semantic segmentation and detection of suitcase handles. Semantic segmentation was initially performed on synthetic data by training a neural network, where it was necessary to set parameters and hyperparameters of the model to achieve it's optimal performance. The ultimate goal was achieved in the sense that the model demonstrated neither overfitting nor underfitting, and it showed constant learning through almost all 1000 epochs. Additionally, with the aim of improving the learning results, additional data sets were generated in the real environment, with slightly different objects compared to the previous ones. Initially, the network was trained on one type of suitcase, while fine-tuning was performed on real data that included a small suitcase, a large suitcase, and their combination. The fine-tuning results brought additional progress, which showed that the model continued to learn even with the new type of data. Then followed the task of detecting the handle of the suitcase, in which three algorithms were implemented. For each algorithm, the implementation is presented, followed by the results and possible future improvements that would advance these algorithms and make them a potential integral part of some other problem in the field of object detection. Algorithms for matching templates and finding contours were rejected as final solutions because they showed certain weaknesses when generating the grip points themselves. The main problem was the variety of data in terms of variable size, orientation and perspective in the original image. Accordingly, the contour defect finding algorithm was chosen as a solution to the problem of detecting the suitcase handle. It proved to be the most robust and generated grip points that in most cases were in the middle of the handle, which was the ultimate goal.