Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN)

Chirgaiya, Sachin; Rajavat, Anand

doi:10.1016/j.iswa.2023.200217

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…This is done by using a model that has been previously trained to classify the types of damage to the car body. By utilizing the YOLO algorithm (Chirgaiya & Rajavat, 2023) and the damage classification model for the car body, technicians can easily and quickly detect and identify car damage with high accuracy. This technology can help improve the efficiency and quality of car maintenance services, as well as reduce the costs required for car maintenance and repair.…”

Section: Methodsmentioning

confidence: 99%

Detects Damage Car Body using YOLO Deep Learning Algorithm

Gustian

Rahman

Hindarto

et al. 2023

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This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.

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Section: Methodsmentioning

confidence: 99%

Detects Damage Car Body using YOLO Deep Learning Algorithm

Gustian

Rahman

Hindarto

et al. 2023

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“…Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN) (Chirgaiya & Rajavat, 2023). The main objective of this research is to increase the average accuracy and average memory of maps.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Suherman

Rahman

Hindarto

et al. 2023

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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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