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Full-Text Articles in Computer Engineering
Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression, Zhihao Zhu, Yan Wang, Zhicheng Ji
Simulation Research On Appearance Detection Of Ampoules Based On Lightweight Network And Model Compression, Zhihao Zhu, Yan Wang, Zhicheng Ji
Journal of System Simulation
Abstract: Aiming at the large scale and redundant parameters of target detection network model, which result in the difficult to deploy the ampoule bottle appearance defect detection model to edge devices, an LC-Faster R-CNN defect detection algorithm based on lightweight network and model compression is proposed. MobileNet-V2 is used as the backbone, and the redundant channels in the convolutional network are trimmed by model pruning strategy. The floating-point parameters are quantized into integers through saturation truncation mapping. Knowledge distillation is used to restore the accuracy of the compressed network. Tested on the self-built ampoule appearance defect dataset, the model volume …
Detection And Classification Of White Blood Cells With An Improved Deep Learning-Based Approach, Fatma Akalin, Nejat Yumuşak
Detection And Classification Of White Blood Cells With An Improved Deep Learning-Based Approach, Fatma Akalin, Nejat Yumuşak
Turkish Journal of Electrical Engineering and Computer Sciences
The analysis of white blood cells, which defend the body against deadly infections and disease-causing substances, is an important issue in the medical world. The concentrations of these cells in the blood, examined in 5 classes, i.e. monocytes, eosinophils, basophils, lymphocytes, and neutrophils, vary according to the types of diseases in the body. The peripheral blood smear is widely used to analyze blood cells. Manual evaluation of this method is laborious and time-consuming. At the same time, many environmental and humanistic parameters affect the method's performance. Therefore, in the presented study, a real-time detection process is realized. Firstly, YOLOv5s, YOLOv5x, …
A Deep Learning Based System For Real-Time Detection And Sorting Of Earthworm Cocoons, Ali̇ Çeli̇k, Si̇nan Uğuz
A Deep Learning Based System For Real-Time Detection And Sorting Of Earthworm Cocoons, Ali̇ Çeli̇k, Si̇nan Uğuz
Turkish Journal of Electrical Engineering and Computer Sciences
Vermicompost, created by earthworms after eating and digesting organic waste, plays an important role as an organic fertiliser in sustainable agriculture. In this study, a deep learning-based smart system was developed to separate earthworm cocoons used in the production of vermicompost from the compost and return it to production. In the first stage of the study, a dataset containing 1000 images of cocoons was created. The cocoons in each image were labeled and training was performed using a deep learning architecture, one-stage and two-stage models. The models were trained over 2000 epochs with a learning rate of 0.01. From the …
Motion-Aware Vehicle Detection In Driving Videos, Mehmet Kiliçarslan, Tansu Temel
Motion-Aware Vehicle Detection In Driving Videos, Mehmet Kiliçarslan, Tansu Temel
Turkish Journal of Electrical Engineering and Computer Sciences
This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes motion frame image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs …