0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Camouflage Object Segmentation with Attention-Guided Pix2Pix and Boundary Awareness
Authors :
Erfan Akbarnezhad Sany
1
Fatemeh Naserizadeh
2
Parsa Sinichi
3
Seyyed Abed Hosseini
4
1- Faculty of Computer Engineering, Quchan University of Technology, Quchan, Iran.
2- Faculty of Electrical & Computer Engineering, Malek Ashtar University of Technology
3- Faculty of Computer Engineering, Quchan University of Technology, Quchan, Iran.
4- Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Keywords :
Camouflage،Object segmentation،Pix2Pix،Aware –boundary
Abstract :
Segmentation of camouflaged objects presents a significant challenge in computer vision due to their inherent blending with the background environment. This paper proposes an innovative method for precise camouflage segmentation using an advanced Pix2Pix architecture. The proposed method leverages the VGG16 network as a backbone for extracting robust features. Subsequently, convolutional block attention modules are integrated to refine these features by focusing attention on spatial and channel dimensions. The generator's decoding pathway employs upsampling stages and skips connections to reconstruct high-resolution segmentation maps effectively. Simultaneously, a discriminator network differentiates between ground truth segmentation maps and the generated ones. To optimize the model's performance, a combination of adversarial, L1, and Soft Dice losses was used during training. Here, the Dice Score loss function was employed twice. Firstly, it was used to quantify the disparity between the ground truth mask and the mask predicted by the generator. Secondly, it was applied to assess the difference between the boundary of the ground truth mask and the boundary of the predicted region extracted by morphological techniques. Comprehensive evaluations of the CAMO dataset demonstrate the effectiveness of the proposed method. This method achieves a pixel accuracy of 85.4054%, intersection over union of 43.9517, F-measure of 58.5606, and mean absolute error of 0.1307. These results highlight advancements in developing robust computer vision systems capable of identifying and understanding camouflage strategies in real-world scenarios.
Papers List
List of archived papers
FaaScaler: An Automatic Vertical and Horizontal Scaler for Serverless Computing Environments
Zahra Rezaei - Saeid Abrishami - Seid Nima Moeintaghavi
Deep Learning Based High-Resolution Edge Detection for Microwave Imaging using a Variational Autoencoder
Seyed Reza Razavi Pour - Leila Ahmadi - Amir Ahmad Shishegar
Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel Robot
Hasan Jalali - Shaya Garjani - Ahmad Kalhor - Mehdi Tale Masouleh - Parisa Yousefi
Soccer Video Event Detection Using Metric Learning
Ali Karimi - Ramin Toosi - Mohammad Ali Akhaee
Reliability Evaluation of 4:2 Compressors Based on Hammock Networks
Farshad Safaei - Mohammad mahdi Emadi Kouchak - Sara Talebpour
A New Inter-layer Similarity metric for link prediction in multilayer networks
Alireza Abdollahpouri - Samira Rafiee
A Cost-Sensitive Genetic Algorithm for Customer Segmentation in Auto Insurances
Alireza Khajenoori - Mohammad Saniee Abadeh - Mohsen Mohammadzadeh
Efficient Sub-Carrier Relationship Extraction for Human Activity Recognition via EEGNet in Wireless Sensing
Siavash Zaravashan - Sadegh ArefiZadeh - Sajjad Torabi
An Overview of Regression Methods in Early Prediction of Movie Ratings
Houmaan Chamani - Zhivar Sourati Hassanzadeh - Behnam Bahrak
PeQa: a Massive Persian Quenstion-Answering and Chatbot Dataset
Fatemeh Zahra Arshia - Mohammad Ali Keyvanrad - Saeedeh Sadat Sadidpour - Sayyid Mohammad Reza Mohammadi
more
Samin Hamayesh - Version 41.7.6