Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects


  • Endang Suherman Universitas Pradita, Serpong, Banten, Indonesia
  • Ben Rahman Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia
  • Djarot Hindarto Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia
  • Handri Santoso Universitas Pradita, Serpong, Banten, Indonesia




CNN, Computer Vision COCO Dataset, End-to-End Object Detection, Object Detection, ResNet-50


Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.

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Al Jaberi, S. M., Patel, A., & AL-Masri, A. N. (2023). Object tracking and detection techniques under GANN threats: A systemic review. Applied Soft Computing, 139, 110224.

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12346 LNCS, 213–229.

Chen, Y., Lin, Y., Xu, X., Ding, J., Li, C., Zeng, Y., Liu, W., Xie, W., & Huang, J. (2022). Classification of lungs infected COVID-19 images based on inception-ResNet. Computer Methods and Programs in Biomedicine, 225, 107053.

Chirgaiya, S., & Rajavat, A. (2023). Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN). Intelligent Systems with Applications, 18(September 2022), 200217.

de Zarzà, I., de Curtò, J., & Calafate, C. T. (2022). Detection of glaucoma using three-stage training with EfficientNet. Intelligent Systems with Applications, 16(September), 1–10.

García-Aguilar, I., García-González, J., Luque-Baena, R. M., & López-Rubio, E. (2023). Automated labeling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks. Pattern Recognition Letters, 167, 45–52.

Kong, L., Wang, J., & Zhao, P. (2022). YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection. IEEE Access, 10, 55546–55564.

Li, B., & Lima, D. (2021). Facial expression recognition via ResNet-50. International Journal of Cognitive Computing in Engineering, 2(January), 57–64.

Ouf, N. S. (2023). Leguminous seeds detection based on convolutional neural networks: Comparison of faster R-CNN and YOLOv4 on a small custom dataset. Artificial Intelligence in Agriculture.

Paymode, A. S., & Malode, V. B. (2022). Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG. Artificial Intelligence in Agriculture, 6, 23–33.

Prabhakaran, K., & Debebe, T. (2023). Skin Cancer Cancer diagnosis diagnosis with with Yolo Yolo Deep Deep Neural Neural Network Network. Procedia Computer Science, 220, 651–658.

Rajeshkumar, G., Braveen, M., Venkatesh, R., Josephin Shermila, P., Ganesh Prabu, B., Veerasamy, B., Bharathi, B., & Jeyam, A. (2023). Smart office automation via faster R-CNN based face recognition and internet of things. Measurement: Sensors, 27(February), 100719.

Santos-Bustos, D. F., Nguyen, B. M., & Espitia, H. E. (2022). Towards automated eye cancer classification via VGG and ResNet networks using transfer learning. Engineering Science and Technology, an International Journal, 35, 101214.

Sarwinda, D., Paradisa, R. H., Bustamam, A., & Anggia, P. (2021). Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Computer Science, 179(2019), 423–431.

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Wahjuni, S., & Nurarifah, H. (2023). Faster RCNN based leaf segmentation using stereo images. Journal of Agriculture and Food Research, 11(November 2022), 100514.

Xue, G., Li, S., Hou, P., Gao, S., & Tan, R. (2023). Research on lightweight Yolo coal gangue detection algorithm based on resnet18 backbone feature network. Internet of Things (Netherlands), 22(March), 100762.


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How to Cite

Suherman, E. ., Rahman, B. ., Hindarto, D. ., & Santoso, H. . (2023). Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1085-1096.

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