The paper introduces DSSD (Deconvolutional Single Shot Detector), an advanced object detection framework that enhances the accuracy of state-of-the-art detectors by incorporating additional context. DSSD combines a Residual-101 classifier with the SSD (Single Shot MultiBox Detector) framework and adds deconvolution layers to improve the detection of small objects and dense scenes. The authors address the challenges of integrating deconvolution and propose a new prediction module to ensure effective learning during training. The model is evaluated on the PASCAL VOC and COCO datasets, demonstrating superior performance in detecting small objects and objects with specific backgrounds. The DSSD model achieves state-of-the-art accuracy while maintaining comparable speed to other detectors. The paper also discusses the effectiveness of different prediction and deconvolution modules through ablation studies and provides insights into the model's performance on various datasets.The paper introduces DSSD (Deconvolutional Single Shot Detector), an advanced object detection framework that enhances the accuracy of state-of-the-art detectors by incorporating additional context. DSSD combines a Residual-101 classifier with the SSD (Single Shot MultiBox Detector) framework and adds deconvolution layers to improve the detection of small objects and dense scenes. The authors address the challenges of integrating deconvolution and propose a new prediction module to ensure effective learning during training. The model is evaluated on the PASCAL VOC and COCO datasets, demonstrating superior performance in detecting small objects and objects with specific backgrounds. The DSSD model achieves state-of-the-art accuracy while maintaining comparable speed to other detectors. The paper also discusses the effectiveness of different prediction and deconvolution modules through ablation studies and provides insights into the model's performance on various datasets.