Scalable Object Detection using Deep Neural Networks (1312.2249v1)

Abstract: Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.

• The paper introduces DeepMultiBox, a novel regression-based method that predicts class-agnostic bounding boxes for efficient, scalable object detection.
• The model employs a two-phase approach that reduces computational overhead while achieving competitive precision on VOC 2007 and ILSVRC 2012 benchmarks.
• The research highlights potential for end-to-end integration and transfer learning, paving the way for robust real-time detection in diverse applications.

Scalable Object Detection using Deep Neural Networks

"Scalable Object Detection using Deep Neural Networks" by Dumitru Erhan, Christian Szegedy, Alexander Toshev, and Dragomir Anguelov proposes a novel method for scalable and efficient object detection leveraging deep neural networks. The paper addresses the significant challenges inherent to object detection, particularly focusing on computational efficiency and scalability over various object classes.

Overview of the Approach

The core approach introduced in this paper is the "DeepMultiBox" model. This method diverges from traditional exhaustive search paradigms by predicting a set of class-agnostic bounding boxes along with associated confidence scores for each box, indicating the likelihood of containing any object of interest. DeepMultiBox incorporates several key elements and contributions:

1. Regression-Based Detection:
   • Unlike traditional methods which utilize predefined boxes for scoring features, DeepMultiBox formulates object detection as a regression problem. The output of the network is the coordinates of predicted bounding boxes along with their confidence scores.
2. Class-Agnostic Detection:
   • The method predicts bounding boxes independently of object classes, allowing for scalability across a large number of classes. This enables the model to generalize object detection across seen and unseen object categories.
3. Efficient Prediction:
   • By utilizing the higher levels of a neural network, the model efficiently narrows down the number of bounding box predictions, reducing the computational overhead typically associated with exhaustive search methods.
4. Optimal Assignment and Training:
   • The training of DeepMultiBox involves solving an assignment problem to best match the predicted boxes with ground truth bounding boxes. The training objective balances the accuracy of detection locations with confidence scores using a combination of match and confidence losses.

Experimental Results and Evaluation

The paper evaluates the performance of DeepMultiBox on the PASCAL Visual Object Classes (VOC) 2007 and ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) 2012 benchmarks. The results illustrate that DeepMultiBox achieves competitive performance:

• VOC 2007:
  • By using the top few predicted locations and combining them with a secondary classification model, the method obtains an average precision (AP) comparable to state-of-the-art methods. Specifically, the achieved mean AP is 0.29, indicating robust detection capabilities with minimal computational cost.
• ILSVRC 2012:
  • On the ImageNet dataset, the DeepMultiBox model demonstrates its scalability by achieving detection@5 performance close to methods that predict one box per class. The precision-recall curves further substantiate the capability of DeepMultiBox to generalize detection across unseen object classes, showcasing its transfer learning potential.

Implications and Future Directions

The research presented in this paper holds significant implications for both practical applications and theoretical advancements in object detection:

1. Computational Efficiency:
   • The reduction in computational complexity by predicting fewer bounding boxes and using a two-phase detection approach allows for the deployment of object detection models in real-time and resource-constrained environments.
2. Scalability:
   • The class-agnostic nature of the model facilitates scaling to a vast number of object classes without a linear increase in parameters or computational cost. This makes the method suitable for applications in large-scale datasets and diverse domains.
3. Potential for Integration:
   • Future developments could focus on integrating the localization and classification networks into a single, end-to-end trainable neural network. Such integration would streamline the detection process further and potentially enhance performance.
4. Transfer Learning:
   • The paper also opens avenues to explore transfer learning applications where models trained on one dataset generalize well to other datasets, thus leveraging knowledge across different datasets for improved detection performance.

In conclusion, the DeepMultiBox model presented in this paper introduces an efficient and scalable approach for object detection using deep neural networks, with competitive performance on benchmark datasets and promising implications for future advancements in the field.