You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: But recent state-of-the-art image classification networks such as Residual Nets (ResNets) [9] and GoogLeNets [24, 26] are by design fully convolutional111Only the last layer is fully-connected, which is removed and replaced when fine-tuning for object detection.. By analogy, it appears natural to use all convolutional layers to construct the shared, convolutional subnetwork in the object detection architecture, leaving the RoI-wise subnetwork no hidden layer. However, as empirically investigated in this work, this naïve solution turns out to have considerably inferior detection accuracy that does not match the network’s superior classification accuracy. To remedy this issue, in the ResNet paper [9] the RoI pooling layer of the Faster R-CNN detector [18] is unnaturally inserted between two sets of convolutional layers — this creates a deeper RoI-wise subnetwork that improves accuracy, at the cost of lower speed due to the unshared per-RoI computation. Analysis. Table 2 shows the results. We note that the standard (not naïve) Faster R-CNN in the ResNet paper [9] achieves 76.4% mAP with ResNet-101 (see also Table 5), which inserts the RoI pooling layer between conv4 and conv5 [9]. As a comparison, the naïve Faster R-CNN (that applies RoI pooling after conv5) has a drastically lower mAP of 68.9% (Table 2). This comparison empirically justifies the importance of respecting spatial information by inserting RoI pooling between layers for the Faster R-CNN system. Similar observations are reported in [19]. Naïve Faster R-CNN. As discussed in the introduction, one may use all convolutional layers in ResNet-101 to compute the shared feature maps, and adopt RoI pooling after the last convolutional layer (after conv5). An inexpensive 21-class fc layer is evaluated on each RoI (so this variant is “almost” fully convolutional). The à trous trick is used for fair comparisons.

Question: How can one explain the statement "inferior detection accuracy that does not match the network’s superior classification accuracy" mentioned by the authors ?
The authors show in Table 2 that a naive implementation of Faster R-CNN, which used the methodology that the authors claimed to have inferior detection accuracy, actually does have inferior detection accuracy.