A scene recognition algorithm based on deep residual network

Abstract

Scene recognition is quite important in the field of robotics and computer vision. Aiming at providing high performance and universality of feature extraction, a convolutional neural network-based scene recognition model entitled Scene-RecNet is proposed. To reduce parameter space and improve the feature quality, deep residual network is introduced as the feature extractor. A feature adjustment layer composed of a convolutional layer and a fully connected layer is added after the feature extractor to further synthesize and compress the extracted features. Migration learning-based 'pre-training and fine-tuning' mode is used to train Scene-RecNet. The feature extractor is pre-trained by Ima-geNet, and the overall network performance is fine-tuned on specific data sets. Experiments show that comparing with other algorithms, the features obtained by Scene-RecNet have high generality and robustness, and Scene-RecNet can provide better scene classification accuracy rate.

Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection

“…Furthermore, the study described in [15] adopted a CNN model based on ResNet50 for classifying tourist sites in Jakarta, Indonesia, like the Cathedral, the Old City of Jakarta, the Istiqlal Mosque and the Maritime Museum. This ResNet-based model also showed exceptional performance in [16] through the proposed method called Scene-RecNet, specially developed to classify aerial scene views.It's also notable that many earlier studies have taken a variant of CNN EfficientNet (EFFNET) architectures to extract features from X-rays to detect lung disease.…”

Landmark Recognition Model for Smart Tourism using Lightweight Deep Learning and Linear Discriminant Analysis

“…To work with 2D CNN classifier (Conv2D), the 1D features matrix was reshaped into 2D features matrix. The top_cov and avg_pool layers in EFFNET produced (16,16,5) and (16,16,245) output shape after being reshaped. Meanwhile, the avg_pool layer of RESNET152 produced (32, 32, 2) feature shape after being reshaped.…”

“…However, the scene recognition is a challenging task due to the difficulty to distinguish the common structure of the public scene objects such building, monuments, parks, beaches and so on [2]. Scene images also might be captured from different angles which triggered the high intra-class difference problems [3].…”

“…Deep learning and transfer learning based classification is the emergence approach in any machine learning tasks [4]- [6]. In scene recognition, the pretrained CNN models by using ResNet50 architecture were adopted [4], [5]. Although the classification accuracy obtained was good (92.17% and 94.4%), ResNet50 produced larger features dimensionality.…”

“…To overcome this problem, the CNN model based on ResNet50 was adopted in [4] to classify tourist attraction places in Jakarta, Indonesia such as Cathedral Church, Jakarta Old Town, Istiqlal Mosque and Maritime Museum. The ResNet based model also demonstrated exceptional performance in [5] via its proposed method namely Scene-RecNet to classify the aerial scene views such as airports, forests and rivers. The Scene-RecNet was more versatile and stable as the features are adjusted and modified in the convolutional and fully-connected layers that eventually improved the processing speed, small storage space and good recognition accuracy.…”