Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction (1803.08707v3)

Abstract: There are mainly two types of state-of-the-art object detectors. On one hand, we have two-stage detectors, such as Faster R-CNN (Region-based Convolutional Neural Networks) or Mask R-CNN, that (i) use a Region Proposal Network to generate regions of interests in the first stage and (ii) send the region proposals down the pipeline for object classification and bounding-box regression. Such models reach the highest accuracy rates, but are typically slower. On the other hand, we have single-stage detectors, such as YOLO (You Only Look Once) and SSD (Singe Shot MultiBox Detector), that treat object detection as a simple regression problem by taking an input image and learning the class probabilities and bounding box coordinates. Such models reach lower accuracy rates, but are much faster than two-stage object detectors. In this paper, we propose to use an image difficulty predictor to achieve an optimal trade-off between accuracy and speed in object detection. The image difficulty predictor is applied on the test images to split them into easy versus hard images. Once separated, the easy images are sent to the faster single-stage detector, while the hard images are sent to the more accurate two-stage detector. Our experiments on PASCAL VOC 2007 show that using image difficulty compares favorably to a random split of the images. Our method is flexible, in that it allows to choose a desired threshold for splitting the images into easy versus hard.

• The paper introduces a hybrid approach that uses image difficulty prediction to allocate tasks between fast single-stage and accurate two-stage detectors.
• It employs a VGG-f based feature extraction and ν-SVR regression to classify images as easy or hard for optimal detector selection.
• Empirical results on PASCAL VOC 2007 show improved mAP and efficiency by balancing detector use based on image complexity.

Optimizing Deep Object Detectors with Image Difficulty Prediction

The paper by Soviany and Ionescu addresses a significant challenge in computer vision: balancing accuracy and speed in object detection. Object detection is pivotal for numerous applications, including autonomous driving, surveillance, and robotics. The research focuses on integrating single-stage and two-stage deep object detectors based on image difficulty, thus seeking an optimal trade-off between accuracy and computational efficiency.

Overview of Object Detectors

Two-stage object detectors like Faster R-CNN and Mask R-CNN are renowned for their accuracy. They categorize images using a Region Proposal Network (RPN) to focus on regions of interest, followed by detailed classification and bounding-box regression. These methods attain high precision but are computationally intensive. Conversely, single-stage detectors such as YOLO and SSD execute detection as a direct regression task, offering significantly faster processing times at the cost of reduced accuracy. This paper proposes a novel approach by leveraging image difficulty prediction to decide the suitability of each approach for given images, thus optimizing performance across these paradigms.

Methodology

Building on curriculum learning principles, the research introduces an image difficulty predictor that categorizes images into "easy" or "hard" and delegates them to the appropriate detector type. The predictor employs deep neural networks regressing human-annotated difficulty scores in images. The easy images are handled by faster single-stage detectors like SSD or MobileNet-SSD, while complex images are deferred to accurate, albeit slower, two-stage detectors like Faster R-CNN.

The methodology is appealing because it treats object detectors as black boxes, avoiding modifications to existing models. Thus, it can directly integrate pre-trained state-of-the-art models into real-world scenarios. The prediction model relies on VGG-f features and linear regression using ν-SVR to classify the difficulty level, facilitating this categorization without extensive computational overhead.

Results and Analysis

Empirical results demonstrate significant improvements in the trade-off between accuracy and speed by deploying the image difficulty predictor on the PASCAL VOC 2007 dataset. A notable observation is that employing image difficulty categorization markedly enhances mAP results compared to random distribution, particularly in balanced configurations like 50%-50% splits between easy and hard images. The easy-versus-hard strategy results in a meaningful increase in precision without substantial increments in processing time.

Furthermore, the paper exemplifies scenarios where distinct predictions by single-stage and two-stage detectors occur, reinforcing their hypothesis regarding difficulty-based categorization efficacy. Difficult images see noticeable accuracy improvements when processed by two-stage models, underscoring the discriminative ability of the predictor to redirect complex tasks effectively.

Implications and Future Work

The proposed methodology is a significant stride in efficient AI deployment scenarios needing both speed and accuracy, such as mobile applications or live object detection feeds. The choice of model based on image difficulty could potentially shift standard practices in model selection and training. These findings call for further investigations into adaptive strategies like difficulty-specific detector training to potentially enhance practical outcomes.

Future research should explore diversified strategies for task dispatching and refine difficulty prediction models to potentially tailor detection frameworks further. Cross-domain applications may unveil additional benefits, suggesting broader implications for this technique in real-time AI implementations.

In conclusion, Soviany and Ionescu's work highlights a practical advancement in optimizing deep object detectors, propelling computational fields toward more intelligent and efficient image processing techniques. Their methodology paves the way for future developments in adaptive AI systems, with potentially transformative impacts on efficiency-centric contexts.