# Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages Source: [Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:08:59.042Z Authors: Yuxi Li, Jiuwei Li, Weiyao Lin, Jianguo Li Published: 2018-07-29T06:58:38.000Z ## Abstract Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, which hinders their wide deployment to many resource restricted usages such as usages on always-on devices, battery-powered low-end devices, etc. This paper considers the resource and accuracy trade-off for resource-restricted usages during designing the whole object detection framework. Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD introduces two innovative and ultra-efficient architecture blocks: depthwise dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN) based front-end. We conduct extensive experiments on three famous benchmarks (PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO, MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD outperforms these solutions in all the three metrics (parameter-size, FLOPs, accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts result with such a low resource requirement. ## 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 2007 (test)](): Object Detection; PASCAL VOC 2007 (test); mAP: 72.1. - [Object Detection on MS COCO (test-dev)](): Object Detection; MS COCO (test-dev); mAP@.5: 40.4. - [Object Detection on COCO (test-dev)](): Object Detection; COCO (test-dev); mAP: 23.2. - [Object Detection on VOC 2007 (test)](): Object Detection; VOC 2007 (test); AP@50: not recorded. ## Original sources and implementation [Original paper]() [PDF]()