# segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection Source: [segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:03:28.249Z Authors: Yukun Zhu, Raquel Urtasun, Ruslan Salakhutdinov, Sanja Fidler Published: 2015-02-15T02:53:56.000Z ## Abstract In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current state-of-the-art, demonstrating the power of our approach. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on PASCAL VOC 2010 (test)](): Object Detection; PASCAL VOC 2010 (test); mAP: 67.2. ## Original sources and implementation [Original paper]() [PDF]()