A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning (1609.04453v1)

Abstract: We have created a large diverse set of cars from overhead images, which are useful for training a deep learner to binary classify, detect and count them. The dataset and all related material will be made publically available. The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation. It is fairly accurate, fast and easy to implement. Additionally, the counting method is not car or scene specific. It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.

• The paper introduces the COWC dataset, a large-scale collection of car images with extensive context, and the ResCeption neural network designed for improved, context-aware car counting from aerial imagery.
• Empirical results show ResCeption achieves superior classification and detection performance compared to traditional architectures while offering high computational efficiency suitable for large-scale real-time applications.
• The COWC dataset and ResCeption network enable more robust models for remote sensing applications like surveillance, urban planning, and advancing dynamic intelligent system analysis.

A Large Contextual Dataset for Classification, Detection, and Counting of Cars with Deep Learning

This paper introduces a large-scale dataset, known as Cars Overhead with Context (COWC), designed to enhance the effectiveness of deep learning models in the classification, detection, and counting of automobiles from aerial imagery. The primary motivation for this dataset's creation emanates from the inadequacies of existing datasets, which suffer from limited diversity, small scale, and restricted applicability across various geographical areas and sensor outputs.

Dataset Overview

The COWC dataset distinguishes itself by incorporating 32,716 unique automobile images sourced from varied global locations and imaging devices, providing a robust platform for developing generalized models. The dataset is enriched with extensive contextual imagery, featuring numerous confounding objects, thus increasing the difficulty and practicality of the machine learning tasks. Negative samples are carefully selected to include objects like trailers and bushes that are visually similar to cars but are not cars, further enhancing the dataset's utility in training discriminative models.

Methodological Innovations

The paper presents ResCeption, a novel neural network architecture that integrates residual learning and Inception-style layers, aimed at leveraging context for comprehensive object counting without relying on localization or density estimation. The ResCeption network demonstrates the capability to execute rapid, accurate one-look object counting, with potential applicability beyond automotive counting to other domains requiring similar analytical capabilities.

Key Findings

Empirical results highlight several significant findings:

• Classification: The ResCeption network achieves superior classification performance, demonstrating accuracy levels that surpass traditional architectures such as AlexNet and benchmark Inception networks. Testing reveals that the inclusion of contextual information marginally improves classification accuracy in challenging scenarios.
• Detection and Localization: The ResCeption model demonstrates strong detection and localization capabilities, outperforming existing systems, particularly in detecting vehicles without explicit localization constraints. The method shows F-scores exceeding those of many established techniques.
• Counting Efficiency: The use of large strides in counting, paired with the ResCeption model, results in computational efficiency that is fit for large-scale deployment, offering operations per pixel that are significantly lower than traditional pixel-striding methods. Quantitative analysis indicates that this approach holds promise for real-time, large-scale applications, such as satellite image analysis for economic and governmental purposes.

Implications and Future Directions

The introduction of this dataset and the associated methodological advancements hold substantial implications for the field of remote sensing and geospatial analysis. By facilitating the creation of more robust, generalized models, the dataset opens avenues for improved surveillance, urban planning, and infrastructure management applications. Additionally, the demonstrated potential of generalized object counting using deep learning expands the horizons for future research into dynamic, context-aware intelligent systems.

Further research could explore the extension of the ResCeption framework to multimodal data types, the enhancement of model adaptability to underrepresented environments or object types, and the refinement of context utilization to further improve model accuracy and reduce computational overhead.

In conclusion, the contributions of this paper serve as a significant step forward in automated car detection and counting from overhead imagery, offering a basis for extensive future exploration and development in the domain of intelligent analysis of aerial and satellite images.